Merge pull request #5 from liuktc/patch-4

Update _graphs.tex

.github/prepare_web_viewer.py

@@ -0,0 +1,68 @@
+import argparse
+from read_metadata import readMetadata
+import re
+import os
+# from pathlib import Path
+# import shutil
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog="Web viewer formatter")
+    parser.add_argument("--src-path", type=str, required=True, help="Path to the .tex sources (for metadata)")
+    parser.add_argument("--out-path", type=str, required=True, help="Path of the output directory")
+    parser.add_argument("--gh-raw-pdf-url", type=str, required=True, help="Base URL of Github raw pdfs")
+    # parser.add_argument("--pdfs-path", type=str, required=True, help="Path of the pdfs directory")
+    parser.add_argument("--template-path", type=str, default="./web-viewer", help="Path to the templates")
+    args = parser.parse_args()
+
+    notes_metadata = readMetadata(args.src_path)
+    table_of_content = ""
+    url_pdf_dir = "pdfs"
+    dest_pdf_dir = os.path.join(args.out_path, url_pdf_dir)
+    with open(os.path.join(args.template_path, "index.html"), "r") as f: index_template = f.read()
+    # with open(os.path.join(args.template_path, "view.html"), "r") as f: viewer_template = f.read()
+
+    os.makedirs(args.out_path, exist_ok=True)
+    # shutil.copytree(args.pdfs_path, dest_pdf_dir, dirs_exist_ok=True)
+
+
+    # Generate home page content
+    for year in sorted(notes_metadata.keys()):
+        for semester in sorted(notes_metadata[year].keys()):
+            for course in sorted(notes_metadata[year][semester]):
+                course_name = notes_metadata[year][semester][course]["name"]
+                course_content = notes_metadata[year][semester][course]["content"]
+
+                if (len(course_content) == 1) and (course_content[0]["name"] is None):
+                    table_of_content += f"<h3><a href='{os.path.join(args.gh_raw_pdf_url, course_content[0]['url'])}'>{course_name}</a></h3>\n"
+                else:
+                    table_of_content += f"<h3>{course_name}</h3>\n"
+                    table_of_content += "<ul>\n"
+                    for content in course_content:
+                        table_of_content += f"<li><h4><a href='{os.path.join(args.gh_raw_pdf_url, content['url'])}'>{content['name']}</a></h4></li>\n"
+                    table_of_content += "</ul>\n"
+
+    with open(os.path.join(args.out_path, "index.html"), "w") as f:
+        f.write(
+            re.sub(
+                r"<!-- begin-toc -->[\s\S]*<!-- end-toc -->", 
+                f"<!-- begin-toc -->\n{table_of_content}\n<!-- end-toc -->", 
+                index_template
+            )
+        )
+
+
+    # Generate viewer content
+    # for year in notes_metadata.keys():
+    #     for semester in notes_metadata[year].keys():
+    #         for course in notes_metadata[year][semester]:
+    #             course_name = notes_metadata[year][semester][course]["name"]
+    #             course_content = notes_metadata[year][semester][course]["content"]
+
+    #             for content in course_content:
+    #                 content_local_path = os.path.join(url_pdf_dir, content["url"])
+    #                 content_html_name = f"{Path(content['url']).stem}.html"
+    #                 with open(os.path.join(args.out_path, content_html_name), "w") as f:
+    #                     page_content = re.sub(r"{{pdf-path}}", f"{content_local_path}", viewer_template)
+    #                     page_content = re.sub(r"{{course-name}}", f"{Path(content['url']).name}", page_content)
+    #                     f.write(page_content)

.github/read_metadata.py

@@ -0,0 +1,30 @@
+import os
+import json
+
+
+def readMetadata(src_path, gh_link="", metadata_file_name="metadata.json"):
+    notes_metadata = {}
+
+    # Reads courses metadata
+    for root, _, files in os.walk(src_path):
+        if metadata_file_name in files:
+            with open(os.path.join(root, metadata_file_name)) as f:
+                metadata = json.load(f)
+                dir_name = os.path.relpath(root, src_path)
+                gh_path = os.path.join(gh_link, dir_name)
+                
+                if metadata["year"] not in notes_metadata: notes_metadata[metadata["year"]] = {}
+                if metadata["semester"] not in notes_metadata[metadata["year"]]: notes_metadata[metadata["year"]][metadata["semester"]] = {}
+
+                notes_metadata[metadata["year"]][metadata["semester"]][metadata["name"]] = {
+                    "name": metadata["name"],
+                    "content": [
+                        {
+                            "name": pdf["name"],
+                            "url": os.path.join(gh_path, pdf["path"])
+                        }
+                        for pdf in metadata["pdfs"]
+                    ]
+                }
+
+    return notes_metadata

.github/update_readme.py

@@ -0,0 +1,94 @@
+import argparse
+from read_metadata import readMetadata
+import re
+import subprocess
+
+
+
+def get_contributors(dir=".", filter_usernames=["NotXia"]):
+    contributors = {}
+    regex_gh_noreply1 = re.compile(r"\s*(?P<commits>\d+)\s+(?P<fullname>.+) <(?P<email>\d+\+(?P<username>.+)@users\.noreply\.github\.com)>")
+    regex_gh_noreply2 = re.compile(r"\s*(?P<commits>\d+)\s+(?P<fullname>.+) <(?P<email>(?P<username>.+)@users\.noreply\.github\.com)>")
+    regex_fallback = re.compile(r"\s*(?P<commits>\d+)\s+(?P<fullname>.+) <(?P<email>.+@.+\.\w+)>")
+
+    p1 = subprocess.Popen(["git", "log"], stdout=subprocess.PIPE)
+    p2 = subprocess.Popen(["git", "shortlog", "-n", "-s", "-e"], stdin=p1.stdout, stdout=subprocess.PIPE)
+    result = p2.communicate()[0].decode("utf-8").strip()
+
+    if len(result) == 0: return []
+
+    for l in result.split("\n"):
+        if ((res := regex_gh_noreply1.search(l)) != None) or ((res := regex_gh_noreply2.search(l)) != None):
+            email = res.group("email")
+            username = res.group("username")
+            fullname = res.group("fullname")
+            commits = int(res.group("commits"))
+        elif (res := regex_fallback.search(l)) != None:
+            email = res.group("email")
+            username = None
+            fullname = res.group("fullname")
+            commits = int(res.group("commits"))
+
+        if username in filter_usernames:
+            continue
+        
+        if email not in contributors: 
+            contributors[email] = {
+                "gh_username": None,
+                "fullnames": [],
+                "commits": 0,
+            }
+        contributors[email]["gh_username"] = username
+        contributors[email]["fullnames"].append(fullname)
+        contributors[email]["commits"] += commits
+
+    contributors = list(contributors.values())
+    contributors.sort(key=lambda x: x["commits"], reverse=True)
+    return contributors
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog="README updater")
+    parser.add_argument("--src-path", type=str, required=True, help="Path to the .tex sources")
+    parser.add_argument("--readme-path", type=str, required=True, help="Path to the readme")
+    parser.add_argument("--gh-link", type=str, required=True, help="Link to the GitHub repo")
+    args = parser.parse_args()
+
+
+    # Adds ToC to README
+    notes_metadata = readMetadata(args.src_path, args.gh_link)
+    with open(args.readme_path, "a") as readme_f:
+        readme_f.write(f"\n\n## Table of contents\n")
+        for year in sorted(notes_metadata.keys()):
+            readme_f.write(f"\n### Year {year}\n")
+
+            for semester in sorted(notes_metadata[year].keys()):
+                for course in sorted(notes_metadata[year][semester]):
+                    course_name = notes_metadata[year][semester][course]["name"]
+                    course_content = notes_metadata[year][semester][course]["content"]
+
+                    if (len(course_content) == 1) and (course_content[0]["name"] is None):
+                        readme_f.write(f"- [**{course_name}**]({course_content[0]['url']})\n")
+                    else:
+                        readme_f.write(f"- **{course_name}**\n")
+                        for content in course_content:
+                            readme_f.write(f"   - [{content['name']}]({content['url']})\n")
+
+
+
+    # Adds contributors to README
+    contributors = get_contributors(args.src_path)
+    with open(args.readme_path, "a") as readme_f:
+        readme_f.write(f"\n\n## Contributors\n")
+        readme_f.write(f"Special thanks for the help to:\n\n")
+        contributors_strs = []
+        for i in range(len(contributors)):
+            if contributors[i]["gh_username"] is not None:
+                contributors_strs.append(
+                    f"[![{contributors[i]['gh_username']}]("
+                    f"https://images.weserv.nl/?url=https://github.com/{contributors[i]['gh_username']}.png&h=50&w&50&mask=circle&fit=cover&maxage=1d"
+                    f")](https://github.com/{contributors[i]['gh_username']})"
+                )
+            elif len(contributors[i]["fullnames"]) > 0:
+                contributors_strs.append(f"{contributors[i]['fullnames'][-1]}")
+        readme_f.write("$\\hspace{1em}$".join(contributors_strs))

.github/web-viewer/index.html

@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/>
+    <meta http-equiv="Pragma" content="no-cache"/>
+    <meta http-equiv="Expires" content="0"/>
+    <title>M.Sc. AI notes</title>
+
+    <style>
+        * {
+            font-family: monospace, monospace;
+        }
+
+        @media (prefers-color-scheme: light) {
+            * {
+                color: black;
+                background-color: white;
+            }
+        }
+
+        @media (prefers-color-scheme: dark) {
+            * {
+                color: white;
+                background-color: #292929;
+            }
+        }
+    </style>
+</head>
+<body>
+
+    <h1>Unibo Master's degree in AI - Notes</h1>
+    <h2>Table of contents</h2>
+    <!-- begin-toc -->
+    <!-- end-toc -->
+</body>
+</html>

.github/web-viewer/view.html

@@ -0,0 +1,23 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{{course-name}}</title>
+    <style>
+        html, body {
+            margin: 0; 
+            padding: 0; 
+            overflow: hidden;
+        }
+    </style>
+</head>
+<body style="height: 100vh; width: 100vw;">
+    <object data="{{pdf-path}}" type="application/pdf" width="100%" height="100%">
+        <p>
+            There has been an error. View the pdf <a href="{{pdf-path}}">here</a>
+        </p>
+    </object>
+</body>
+
+</html>

.github/workflows/compile.yml

@@ -4,6 +4,17 @@ on:
   push:
     branches:
       - main
+    paths: [
+      "src/**", 
+      .github/update_readme.py, 
+      .github/read_metadata.py, 
+      .github/workflows/compile.yml, 
+      compile.sh
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
 
 jobs:
   compile:
@@ -12,37 +23,43 @@ jobs:
 
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: Install LaTeX
+      - name: Checkout current pdfs branch
+        uses: actions/checkout@v4
+        with:
+          ref: pdfs
+          path: .currpdfs
+
+
+      - name: Prepare output directory
         run: |
-            sudo apt update
-            sudo apt install -y latexmk texlive texlive-science texlive-latex-extra
+          mkdir ${GITHUB_WORKSPACE}/.compiled
+          cp ${GITHUB_WORKSPACE}/LICENSE-pdf ${GITHUB_WORKSPACE}/.compiled/LICENSE
 
       - name: Compile
         run: |
-            shopt -s globstar
-            work_dir=$(pwd)
-            mkdir /tmp/compiled
+          docker pull ghcr.io/notxia/unibo-ai-notes:main
+          docker run -v ${GITHUB_WORKSPACE}:/notes ghcr.io/notxia/unibo-ai-notes:main
 
-            for f in **/[!_]*.tex; do
-                f_dir=$(dirname $f);
-                f_base=$(basename $f);
-                f_nameonly="${f_base%.*}";
-
-                cd ${f_dir};
-                latexmk -pdf -jobname=${f_nameonly} ${f_base};
-                mkdir -p /tmp/compiled/${f_dir}
-                mv ${f_nameonly}.pdf /tmp/compiled/${f_dir}/.
-                cd $work_dir;
-            done
-             
+      - name: Generate README
+        run: |
+          cp README.md .compiled/README.md
+          python3 ${GITHUB_WORKSPACE}/.github/update_readme.py \
+            --src-path ${GITHUB_WORKSPACE}/src \
+            --readme-path ${GITHUB_WORKSPACE}/.compiled/README.md \
+            --gh-link https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs
 
       - name: Move to pdfs branch
         uses: s0/git-publish-subdir-action@develop
         env:
           REPO: self
           BRANCH: pdfs
-          FOLDER: /tmp/compiled
+          FOLDER: .compiled
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          MESSAGE: "🤖 It's hard to work with you"
+          COMMIT_NAME: "github-actions[bot]"
+          COMMIT_EMAIL: "github-actions[bot]@users.noreply.github.com"
+          MESSAGE: "🤖 Hello human, trying to not break anything ({sha})"
+          SKIP_EMPTY_COMMITS: true

.github/workflows/notes-registry.yml

@@ -0,0 +1,49 @@
+name: Create and publish Docker image
+
+on:
+    push:
+      branches:
+        - main
+      paths: [
+        Dockerfile
+      ]
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Container registry login
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push image
+        id: push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/web.yml

@@ -0,0 +1,42 @@
+name: Setup web viewer
+
+on:
+  push:
+    branches:
+      - main
+    paths: [
+      "src/**", 
+      .github/prepare_web_viewer.py, 
+      .github/workflows/web.yml, 
+    ]
+
+jobs:
+  compile:
+    name: Format web pages
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+        
+      - name: Generate web viewer content
+        run: |
+          python3 ${GITHUB_WORKSPACE}/.github/prepare_web_viewer.py \
+            --src-path=${GITHUB_WORKSPACE}/src \
+            --out-path=/tmp/webviewer \
+            --template-path=${GITHUB_WORKSPACE}/.github/web-viewer \
+            --gh-raw-pdf-url="https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs"
+
+      - name: Move to pages branch
+        uses: s0/git-publish-subdir-action@develop
+        env:
+          REPO: self
+          BRANCH: pages
+          FOLDER: /tmp/webviewer
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COMMIT_NAME: "github-actions[bot]"
+          COMMIT_EMAIL: "github-actions[bot]@users.noreply.github.com"
+          MESSAGE: "🤖 Web developer stuff"
+          SKIP_EMPTY_COMMITS: true

.gitignore

@@ -0,0 +1,19 @@
+*.synctex.gz
+*.synctex(busy)
+*.log
+*.fls
+*.fdb_latexmk
+*.aux
+*.toc
+*.out
+*.bbl
+*.bcf
+*.blg
+*.run.xml
+[!_]*.pdf
+
+.compiled
+
+__pycache__
+
+.vscode

Dockerfile

@@ -0,0 +1,11 @@
+FROM archlinux:latest
+
+WORKDIR /notes
+
+RUN pacman --noconfirm -Sy
+RUN pacman --noconfirm -S git
+RUN pacman --noconfirm -S texlive-basic texlive-latex texlive-binextra texlive-mathscience texlive-latexextra texlive-fontsextra texlive-bibtexextra biber
+
+RUN git config --global --add safe.directory /notes
+
+CMD ["bash", "./utils/compile.sh", "./src", "./.compiled", "./.currpdfs"]

LICENSE-pdf

@@ -0,0 +1,427 @@
+Attribution-ShareAlike 4.0 International
+
+=======================================================================
+
+Creative Commons Corporation ("Creative Commons") is not a law firm and
+does not provide legal services or legal advice. Distribution of
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+warranties regarding its licenses, any material licensed under their
+terms and conditions, or any related information. Creative Commons
+disclaims all liability for damages resulting from their use to the
+fullest extent possible.
+
+Using Creative Commons Public Licenses
+
+Creative Commons public licenses provide a standard set of terms and
+conditions that creators and other rights holders may use to share
+original works of authorship and other material subject to copyright
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+
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README.md

@@ -1 +1,8 @@
-# Unibo Master's in Artificial Intelligence Notes
+# Unibo M.Sc. AI Notes
+
+My notes for the courses of the Master's Degree in Artificial Intelligence at the University of Bologna.
+
+LaTeX source files are in the [`main` branch](https://github.com/NotXia/unibo-ai-notes/tree/main).\
+Compiled PDF files are in the [`pdfs` branch](https://github.com/NotXia/unibo-ai-notes/tree/pdfs).
+
+**Note**: I'm terrible at taking notes. Please, consider ~~double~~ triple checking anything you plan to use.

src/ainotes.cls

@@ -0,0 +1,150 @@
+\NeedsTeXFormat{LaTeX2e}[]
+\ProvidesClass{ainotes}
+
+\LoadClass{scrreprt}
+
+
+\usepackage{geometry}
+\usepackage{graphicx, xcolor}
+\usepackage{amsmath, amsfonts, amssymb, amsthm, mathtools, bm, upgreek, cancel, bbm, siunitx, thmtools}
+\usepackage[bottom]{footmisc}
+\usepackage[pdfusetitle]{hyperref}
+\usepackage[nameinlink]{cleveref}
+\usepackage[all]{hypcap} % Links hyperref to object top and not caption
+\usepackage[inline]{enumitem}
+\usepackage{marginnote}
+\usepackage{scrlayer-scrpage}
+\usepackage{scrhack, algorithm, listings}
+\usepackage{array, makecell, multirow, booktabs}
+\usepackage{acro}
+\usepackage{subcaption}
+\usepackage{eurosym}
+\usepackage{bussproofs} % Deductive tree
+\usepackage{varwidth} 
+\usepackage[most]{tcolorbox}
+\usepackage{tikz}
+\tcbuselibrary{breakable}
+\usetikzlibrary{decorations.pathmorphing,calc}
+
+\geometry{ margin=3cm, lmargin=1.5cm, rmargin=4.5cm, marginparwidth=3cm }
+\hypersetup{ colorlinks, citecolor=black, filecolor=black, linkcolor=black, urlcolor=black, linktoc=all }
+
+\definecolor{codegreen}{rgb}{0,0.6,0}
+\definecolor{codegray}{rgb}{0.5,0.5,0.5}
+\definecolor{codepurple}{rgb}{0.58,0,0.82}
+\definecolor{backcolour}{rgb}{0.95,0.95,0.92}
+\lstdefinestyle{mystyle}{
+    commentstyle = \color{codegreen},
+    keywordstyle = \color{magenta},
+    numberstyle = \tiny\color{codegray},
+    stringstyle = \color{codepurple},
+    basicstyle = \footnotesize\ttfamily,
+    breakatwhitespace = false, 
+    breaklines = true, 
+    captionpos = b, 
+    keepspaces = true,
+    numbers = none,
+    showspaces = false,
+    showstringspaces = true,
+    showtabs = false,
+    tabsize = 3
+}
+\lstset{style=mystyle}
+\lstset{language=Python}
+
+
+\NewDocumentEnvironment{descriptionlist}{}{%
+    \begin{description}[labelindent=1em]
+}{
+    \end{description}%
+}
+\setlength{\parindent}{0pt}
+\renewcommand*{\marginfont}{\color{gray}\footnotesize}
+\renewcommand*\chapterpagestyle{scrheadings} % Header in chapter pages
+
+
+\theoremstyle{definition}
+\newtheorem{theorem}{Theorem}[section]
+\newtheorem{corollary}{Corollary}[theorem]
+\newtheorem{lemma}[theorem]{Lemma}
+\newtheorem*{privateexample}{Example}
+\theoremstyle{definition}
+\newtheorem*{definition}{Def}
+\newtheorem*{privateremark}{Remark}
+
+\newtcolorbox{marginbar}[3]{ % #1: color | #2: (number of lines - 1) | #3: line thickness
+    enhanced, blank, breakable,
+    overlay = {
+        \foreach \t in {0,...,#2}{
+            \draw[decorate, #3, #1]
+                ([xshift=-3-\t mm]frame.north west)
+                --
+                ([xshift=-3-\t mm]frame.south west);
+        }
+    }
+}
+
+\newenvironment{example}{%
+    \begin{marginbar}{lightgray}{0}{thick}
+    \begin{privateexample}
+}{%
+    \end{privateexample}
+    \end{marginbar}
+}
+
+\newenvironment{remark}{%
+    \begin{marginbar}{darkgray}{0}{thick}
+    \begin{privateremark}
+}{%
+    \end{privateremark}
+    \end{marginbar}
+}
+
+\def\indenttbox{\hangindent0.5em\hangafter=0}
+
+\newcommand{\ubar}[1]{\text{\b{$#1$}}}
+\renewcommand{\vec}[1]{{\bm{\mathbf{#1}}}}
+\newcommand{\nullvec}[0]{\bar{\vec{0}}}
+\newcommand{\matr}[1]{{\bm{#1}}}
+\newcommand{\prob}[1]{{\mathcal{P}\left({#1}\right)}}
+
+
+\renewcommand*{\maketitle}{%
+    \begin{titlepage}
+    \newgeometry{margin=3cm}
+        \centering
+        \vspace*{\fill}
+        \huge
+        \href{\giturl}{\textbf{\@title}}\\
+        {\Large Last update: \lastupdate}
+        \vspace*{\fill}
+
+        \Large
+        Academic Year \@date\\
+        Alma Mater Studiorum $\cdot$ University of Bologna
+        \vspace*{1cm}
+    \restoregeometry
+    \end{titlepage}
+    \newpage
+}
+
+
+\newcommand*{\makenotesfront}{%
+    \newgeometry{margin=3cm}
+    \maketitle
+    \pagenumbering{roman}
+    \tableofcontents
+    \restoregeometry
+    \newpage
+    \pagenumbering{arabic}
+}
+
+
+\newcommand{\gitFAIKROne}[0]{   https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/fundamentals-of-ai-and-kr/module1            }
+\newcommand{\gitFAIKRTwo}[0]{   https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/fundamentals-of-ai-and-kr/module2            }
+\newcommand{\gitFAIKRThree}[0]{ https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/fundamentals-of-ai-and-kr/module3            }
+\newcommand{\gitLAAITwo}[0]{    https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/languages-and-algorithms-for-ai/module2      }
+\newcommand{\gitMLDM}[0]{       https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/machine-learning-and-data-mining             }
+\newcommand{\gitSMM}[0]{        https://github.com/NotXia/unibo-ai-notes/tree/pdfs/year1/statistical-and-mathematical-methods-for-ai  }
+
+\newcommand{\eoc}[0]{\begin{flushright}\texttt{\raggedleft\small <end of course>}\end{flushright}}

src/year1/cognition-and-neuroscience/metadata.json

@@ -0,0 +1,15 @@
+{
+    "name": "Cognition and Neuroscience",
+    "year": 1,
+    "semester": 2,
+    "pdfs": [
+        {
+            "name": "CN module 1",
+            "path": "module1/cn1.pdf"
+        },
+        {
+            "name": "CN module 2",
+            "path": "module2/cn2.pdf"
+        }
+    ]
+}

src/year1/cognition-and-neuroscience/module1/ainotes.cls

@@ -0,0 +1 @@
+../../../ainotes.cls

src/year1/cognition-and-neuroscience/module1/cn1.tex

@@ -0,0 +1,43 @@
+\documentclass[11pt]{ainotes}
+
+\title{Cognition and Neuroscience\\(Module 1)}
+\date{2023 -- 2024}
+\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}}
+\def\giturl{{PLACEHOLDER-GIT-URL}}
+
+\DeclareAcronym{psp}{short=PSP, long=postsynaptic potential, long-plural=s}
+\DeclareAcronym{epsp}{short=EPSP, long=excitatory postsynaptic potential, long-plural=s}
+\DeclareAcronym{ipsp}{short=IPSP, long=inhibitory postsynaptic potential, long-plural=s}
+\DeclareAcronym{ap}{short=AP, long=action potential, long-plural=s}
+\DeclareAcronym{cns}{short=CNS, long=central nervous system}
+\DeclareAcronym{pns}{short=PNS, long=peripheral nervous system}
+\DeclareAcronym{rl}{short=RL, long=reinforcement learning}
+\DeclareAcronym{nr}{short=NR, long=no response}
+\DeclareAcronym{us}{short=US, long=unconditioned stimulus}
+\DeclareAcronym{ur}{short=UR, long=unconditioned response}
+\DeclareAcronym{cs}{short=CS, long=conditioned stimulus}
+\DeclareAcronym{cr}{short=CR, long=conditioned response}
+
+\newtheorem*{privatecasestudy}{Case study}
+\newenvironment{casestudy}{%
+    \begin{marginbar}{olive}{0}{thick}
+    \begin{privatecasestudy}
+}{%
+    \end{privatecasestudy}
+    \end{marginbar}
+}
+
+\begin{document}
+    
+    \makenotesfront
+    \printacronyms
+    \newpage
+
+    \input{./sections/_introduction.tex}
+    \input{./sections/_nervous_system.tex}
+    \input{./sections/_rl.tex}
+    \input{./sections/_pavlovian_learning.tex}
+    \input{./sections/_instrumental_learning.tex}
+    \eoc
+    
+\end{document}

src/year1/cognition-and-neuroscience/module1/sections/_instrumental_learning.tex

@@ -0,0 +1,605 @@
+\chapter{Instrumental/operant learning}
+
+
+Form of control learning that aims to learn action-outcome associations:
+\begin{itemize}
+    \item When a reinforcer is likely to occur.
+    \item Which actions bring to those reinforcers.
+\end{itemize}
+This allows the animal to act in anticipation of a reinforcer.
+
+Instrumental learning includes:
+\begin{descriptionlist}
+    \item[Habitual system] \marginnote{Habitual system}
+        Learn to repeat previously successful actions.
+    \item[Goal-directed system] \marginnote{Goal-directed system}
+        Evaluate actions based on their anticipated consequences.
+\end{descriptionlist}
+
+Depending on the outcome, the effect varies:
+\begin{descriptionlist}
+    \item[Positive reinforcement] \marginnote{Positive reinforcement}
+        Delivering an appetitive outcome to an action increases the probability of emitting it.
+
+    \item[Positive punishment] \marginnote{Positive punishment}
+        Delivering an aversive outcome to an action decreases the probability of emitting it.
+    
+    \item[Negative reinforcement] \marginnote{Negative reinforcement}
+        Omitting an aversive outcome to an action increases the probability of emitting it.
+    
+    \item[Negative punishment] \marginnote{Negative punishment}
+        Omitting an appetitive outcome to an action decreases the probability of emitting it.
+\end{descriptionlist}
+
+\begin{table}[H]
+    \centering
+    \begin{tabular}{r|cc}
+        \toprule
+                            & \textbf{Delivery}                         & \textbf{Omission} \\
+        \midrule
+        \textbf{Appetitive} & Positive reinforcement (\texttt{+prob})   & Negative punishment (\texttt{-prob}) \\
+        \textbf{Aversive}   & Positive punishment (\texttt{-prob})      & Negative reinforcement (\texttt{+prob}) \\
+        \bottomrule
+    \end{tabular}
+    \caption{Summary of the possible effects}
+\end{table}
+
+
+
+\section{Types of schedule}
+
+There are two types of learning:
+\begin{descriptionlist}
+    \item[Continuous schedule] \marginnote{Continuous schedule}
+        The desired action is followed by the outcome every time.
+        \begin{remark}
+            More effective to teach a new association.
+        \end{remark}
+
+    \item[Partial schedule] \marginnote{Partial schedule}
+        The desired action is not always followed by the outcome.
+        \begin{remark}
+            Learning is slower but the response is more resistant to extinction.
+        \end{remark}
+
+        There are four types of partial schedules:
+        \begin{descriptionlist}
+            \item[Fixed-ratio] 
+                Outcome available after a specific number of responses.
+
+                This results in a high and steady rate of response, with a brief pause after the outcome is delivered.
+
+
+            \item[Variable-ratio] 
+                Outcome available after an unpredictable number of responses.
+
+                This results in a high and steady rate of response.
+
+
+            \item[Fixed-interval] 
+                Outcome available after a specific interval of time.
+
+                This results in a high rate of response near the end of the interval and a slowdown after the outcome is delivered.
+
+
+            \item[Variable-interval] 
+                Outcome available after an unpredictable interval of time.
+
+                This results in a slow and steady rate of response.
+        \end{descriptionlist}
+\end{descriptionlist}
+
+\begin{minipage}{0.55\linewidth}
+    \begin{casestudy}[Aplysia Californica]
+        An Aplysia Californica will withdraw its gill upon stimulating the siphon.
+        \begin{itemize}
+            \item Repeated mild stimulations will induce a habituation of the reflex.
+            \item Repeated intense stimulations will induce a sensitization of the reflex.
+        \end{itemize}
+    \end{casestudy}
+\end{minipage}
+\begin{minipage}{0.4\linewidth}
+    \centering
+    \includegraphics[width=0.9\linewidth]{./img/gill_habituation.png}
+\end{minipage}
+
+
+
+\section{Dopamine}
+
+There is evidence that dopamine is involved in learning action-outcome associations.
+
+\begin{description}
+    \item[Striatal activity on unexpected events] \marginnote{Striatal activity on unexpected events}
+        When an unexpected event happens, there is a change in the activity of the striatum.
+        There is an increase in response when the feedback is positive and a decrease when negative.
+
+        \begin{casestudy}[Microelectrodes in substantia nigra]
+            \phantom{}\\
+            \begin{minipage}{0.7\linewidth}
+                The activity of the substantia nigra of patients with Parkinson's disease is measured during a probabilistic instrumental learning task.
+                The task consists of repeatedly drawing a card from two decks, followed by positive or negative feedback depending on the deck.
+            \end{minipage}
+            \begin{minipage}{0.28\linewidth}
+                \centering
+                \includegraphics[width=0.95\linewidth]{./img/instrumental_dopamine_sn1.png}
+            \end{minipage}
+
+            The increase and decrease in striatal activity can be clearly seen when the feedback is unexpected.
+            \begin{figure}[H]
+                \centering
+                \includegraphics[width=\linewidth]{./img/instrumental_dopamine_sn2.png}
+            \end{figure}
+        \end{casestudy}
+
+    \item[Dopamine effect on behavior] \marginnote{Dopamine effect on behavior}
+        The amount of dopamine changes the learning behavior:
+        \begin{itemize}
+            \item Low levels of dopamine cause an impairment in learning from positive feedback.
+                This happens because positive prediction errors cannot occur.
+            
+            \item High levels of dopamine cause an impairment in learning from negative feedback.
+                This happens because negative prediction errors cannot occur.
+        \end{itemize}
+
+        \begin{casestudy}[Probabilistic selection task]
+            This instrumental learning task has two phases:
+            \begin{descriptionlist}
+                \item[Learning]
+                    There are three pairs of stimuli (symbols) and, at each trial, a pair is presented to the participant who selects one.
+                    For each pair, a symbol has a higher probability of providing positive feedback while the other is more likely to be negative.
+                    Moreover, the probabilities are different among the three pairs.
+
+                    \begin{center}
+                        \includegraphics[width=0.55\linewidth]{./img/instrumental_dopamine_selection1.png}
+                    \end{center}
+
+                    Participants are required to learn by trial and error the stimulus in each pair that leads to a positive reward.
+                    Note that learning could be accomplished by:
+                    \begin{itemize}
+                        \item Recognizing the more rewarding stimulus.
+                        \item Recognizing the less rewarding stimulus.
+                        \item Both.
+                    \end{itemize}
+
+                \item[Testing]
+                    Aims to assess if participants learned to select positive feedback or avoid negative feedback.
+
+                    The same task as above is repeated but all combinations of the stimuli among the three pairs are possible.
+            \end{descriptionlist}
+
+            Three groups of participants are considered for this experiment:
+            \begin{enumerate}
+                \item Those who took the cabergoline drug (dopamine antagonist).
+                \item Those who took the haloperidol drug (dopamine agonist).
+                \item Those who took a drug without effects (placebo).
+            \end{enumerate}
+
+            \begin{center}
+                \includegraphics[width=0.55\linewidth]{./img/instrumental_dopamine_selection2.png}
+            \end{center}
+
+            Results show that:
+            \begin{enumerate}
+                \item Cabergoline inhibited positive feedback learning.
+                \item Haloperidol enhanced positive feedback learning.
+                \item Placebo learned positive and negative feedback equally.
+            \end{enumerate}
+        \end{casestudy}
+        
+        \begin{casestudy}
+            It has been observed that:
+            \begin{itemize}
+                \item Reward prediction errors are correlated with activity in the left posterior putamen and left ventral striatum.
+                \item Punishment prediction errors are correlated with activity in the right anterior insula.
+            \end{itemize}
+
+            \begin{center}
+                \includegraphics[width=0.5\linewidth]{./img/pe_location.png}
+            \end{center}
+        \end{casestudy}
+
+    \item[Actor-critic model] \marginnote{Actor-critic model}
+        Model to correlate Pavlovian and instrumental learning. 
+        It is composed by:
+        \begin{itemize}
+            \item The cortex is responsible for representing the current state.
+            \item The basal ganglia implement two computational models:
+                \begin{descriptionlist}
+                    \item[Critic] \marginnote{Critic}
+                        Learns stimulus-outcome associations and is active in both Pavlovian and instrumental learning.
+                        It might be implemented in the ventral striatum, the amygdala and the orbitofrontal cortex.
+                
+                    \item[Actor] \marginnote{Actor}
+                        Learns stimulus-action associations and is only active during instrumental learning.
+                        It might be implemented in the dorsal striatum.
+                \end{descriptionlist}
+        \end{itemize}
+\end{description}
+
+\begin{casestudy}[Food and cocaine]
+    \phantom{}
+    \begin{itemize}
+        \item Food-induced dopamine response is modulated by the reward expectations that promote learning until the prediction matches the actual outcome.
+        \item Cocaine-induced dopamine response causes a continuous increase in the predicted reward that
+            will eventually surpass all other cues and bias decision-making towards cocaine.
+    \end{itemize}
+    \begin{center}
+        \includegraphics[width=0.8\linewidth]{./img/dopamine_food_cocaine.png}
+    \end{center}
+\end{casestudy}
+
+
+
+\section{Learning strategies historical evolution}
+
+
+% Instrumental learning can happen in two ways:
+% \begin{descriptionlist}
+%     \item[Cognitive map] \marginnote{Cognitive map}
+%         Actions are taken based on the expected reward.
+    
+%     \item[Response strategy] \marginnote{Response strategy}
+%         Actions are associated with particular stimuli.
+% \end{descriptionlist}
+
+
+\subsection{Generation 0}
+
+There were two possible learning strategies:
+\begin{descriptionlist}
+    \item[Stimulus-response theory] \marginnote{Stimulus-response theory}
+        Learning happens by creating stimulus-response associations.
+
+        Learning does not happen if there is no reward.
+
+    \item[Cognitive map / Field theory] \marginnote{Cognitive map / Field theory}
+        A mental map is created and used to find the best action in a given state based on the expected reward.
+
+        \begin{description}
+            \item[Latent learning] \marginnote{Latent learning}
+                Learning that is not shown behaviorally unless there is enough motivation.
+        \end{description}
+\end{descriptionlist}
+
+\begin{casestudy}[Maze]
+    An animal is put at the start of a maze where a reward is located in the west arm.
+    After some training iterations, the animal is put at the other entrance:
+    \begin{itemize}
+        \item If it goes to the west arm, it learned to solve the maze using a cognitive map/place strategy.
+        \item If it goes to the east arm, it learned to solve the maze using a stimulus-response strategy.
+    \end{itemize}
+    \begin{center}
+        \includegraphics[width=0.55\linewidth]{./img/instrumental_maze.png}
+    \end{center}
+
+    It has been observed that rats start by learning a cognitive map (i.e. the environment is unknown).
+    After enough training, they start relying on a response strategy (i.e. the environment is stable).
+\end{casestudy}
+
+\begin{casestudy}[Tolman's maze]
+    Consider a maze with curtains and doors to prevent a long-distance perspective.
+    \begin{center}
+        \includegraphics[width=0.35\linewidth]{./img/tolman_maze.png}
+    \end{center}
+
+    Two groups of hungry rats have been considered to solve the maze:
+    \begin{descriptionlist}
+        \item[Group 1] No reward for solving the maze.
+        \item[Group 2] Reward for solving the maze.
+    \end{descriptionlist}
+    It has been shown that the second group completes the maze faster.
+    \begin{center}
+        \includegraphics[width=0.45\linewidth]{./img/tolman_experiment1.png}
+    \end{center}
+
+    To show latent learning, three groups of hungry rats have been considered:
+    \begin{descriptionlist}
+        \item[Group 1] No reward for solving the maze.
+        \item[Group 2] Reward for solving the maze.
+        \item[Group 3] Reward for solving the maze starting from day 11.
+    \end{descriptionlist}
+    It has been shown that rats of the third group complete the maze faster as soon as they receive food.
+    \begin{center}
+        \includegraphics[width=0.45\linewidth]{./img/tolman_experiment2.png}
+    \end{center}
+\end{casestudy}
+
+
+\subsection{Generation 1} 
+
+Shifted from studying the spatial domain to a more general domain.
+Based on two types of actions:
+\begin{descriptionlist}
+    \item[Goal-directed action] \marginnote{Goal-directed action}
+        Actions made because a desired outcome is expected.
+        An action is goal-directed if:
+        \begin{itemize}
+            \item There is knowledge of the relationship between action and consequences (response-outcome).
+            \item The outcome is motivationally relevant.
+        \end{itemize}
+
+        Goal-directed behavior has the following properties:
+        \begin{itemize}
+            \item Involves active deliberation.
+            \item Has a high computational cost.
+            \item It is flexible to changes of the environmental contingency (i.e. stops if no reward occurs)
+        \end{itemize}
+
+        \begin{center}
+            \includegraphics[width=0.65\linewidth]{./img/goal_directed_behavior.png}
+        \end{center}
+
+    \item[Habitual action] \marginnote{Habitual action}
+        Actions made automatically just because they were rewarded in the past.
+        They are not influenced by the current outcome even if it is undesired.
+
+        Habitual behavior has the following properties:
+        \begin{itemize}
+            \item Does not require active deliberation.
+            \item Has a low computational cost.
+            \item It is inflexible to changes of the environmental contingency.
+        \end{itemize}
+
+        \begin{center}
+            \includegraphics[width=0.65\linewidth]{./img/habitual_behavior.png}
+        \end{center}
+
+\end{descriptionlist} 
+
+\begin{casestudy}[Goal-directed vs habitual behavior]
+    The experiment is done in three steps:
+    \begin{descriptionlist}
+        \item[Training] 
+            The animal undergoes instrumental learning (e.g. associate that by pressing a lever some food will be dropped).
+        
+        \item[Devaluation] 
+            Manipulate the learned behavior by either:
+            \begin{itemize}
+                \item Devaluate the reinforcer.
+                \item Degradate the contingency.
+            \end{itemize}
+
+        \item[Testing] 
+            Repeat the training scenario without reward:
+            \begin{itemize}
+                \item If the action associated with a devaluated reinforcer is performed less, the behavior is goal-directed.
+                \item If the frequency of the action is the same, the behavior is habitual.
+            \end{itemize}
+    \end{descriptionlist}
+
+    \indenttbox
+    \begin{remark}
+        The training phase aims to instill a goal-directed behavior.
+        On the other hand, if the animal is overtrained, it will learn a habitual behavior.
+        The experiment can be done both ways.
+    \end{remark}
+
+    \begin{center}
+        \includegraphics[width=0.85\linewidth]{./img/goal_directed_vs_habitual.png}
+    \end{center}
+\end{casestudy}
+
+It has been hypothesized that the striatum might be the interface where rewards influence actions as: \marginnote{Striatum}
+\begin{itemize}
+    \item The basal ganglia are involved in the selection of actions.
+    \item The SNc affects the plasticity of the striatum through the release of dopamine.
+\end{itemize}
+
+Moreover, different sections of the striatum are responsible for different types of behavior:
+\begin{descriptionlist}
+    \item[Dorsomedial striatum] Supports goal-directed behavior.
+    \item[Dorsolateral striatum] Supports habitual behavior.
+\end{descriptionlist}
+This also hints that goal-directed and habitual behaviors act simultaneously and competitively (see \hyperref[sec:instrumental_gen3]{Generation 3}).
+
+
+\subsection{Generation 2} 
+
+Studied goal-directed and habitual behavior in humans.
+
+\begin{description}
+    \item[Functional magnetic resonance imaging (fRMI)] \marginnote{Functional magnetic resonance imaging (fRMI)}
+        Measures the ratio of oxygenated to deoxygenated hemoglobin molecules in the brain.
+        It allows to indirectly measure the neuronal activity of the brain on a high spatial resolution (i.e. allows to see where things happen but not when).
+\end{description}
+
+\begin{casestudy}[Goal-directed behavior in humans]
+    Candidates are trained to select between two fractals of which 
+    one leads to a reward with a high probability and the other with a low probability.
+    The possible rewards are chocolate, tomato juice and orange juice (used as a control outcome).
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.45\linewidth]{./img/human_goal_directed_experiment.png}
+        \caption{
+            Structure of the task. The high probability choice leads to the primary reward (chocolate or tomato juice) with probability $0.4$, 
+            to the control reward (orange juice) with probability $0.3$ and to nothing with probability $0.3$.
+            The low probability choice leads to the control reward with probability $0.3$ and nothing in the other cases.
+            The neutral case leads to an empty glass or nothing.
+        }
+    \end{figure}
+
+    After training, one of the primary rewards is devalued through selective satiation and the other is labeled as the valued outcome.
+    Then, the training task is repeated.
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.55\linewidth]{./img/human_goal_directed_experiment2.png}
+        \caption{
+            Steps of the experiment. In this figure, the devalued reward is the tomato juice.        
+        }
+    \end{figure}
+
+    Behavioral results show that:
+    \begin{itemize}
+        \item During training, candidates favored the high-probability actions associated with chocolate and tomato juice.
+            On the other hand, choices for the neutral condition were evenly distributed.
+        \item The pleasantness rating for the devalued reward lowered after devaluation while the valued reward remained higher.
+        \item During testing, candidates reduced their choice of the high-probability action associated with the devalued reward.
+    \end{itemize}
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.9\linewidth]{./img/human_goal_directed_experiment3.png}
+    \end{figure}
+
+    During both training and testing, the fRMIs of the candidates were taken.
+    Neural results show that the \textbf{medial orbitofrontal cortex }has a significant modulation in its activity during instrumental action selection
+    depending on the value of the associated outcome.
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.4\linewidth]{./img/human_goal_directed_experiment4.png}
+    \end{figure}
+\end{casestudy}
+
+\begin{casestudy}[Habitual behavior in humans]
+    Candidates are presented, at each round of the trial, with a fractal image and a schematic indicating which button to press.
+    The button can be pressed an arbitrary number of times and, at each press, on the screen appears:
+    \begin{itemize}
+        \item A gray circle (no reward).
+        \item The image of an M\&M's {\tiny ©} or Frito {\tiny ©} (reward) with probability $0.1$. 
+            To each fractal, only a type of reward can appear.
+    \end{itemize} 
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.6\linewidth]{./img/human_habitual_experiment.png}
+    \end{figure}
+
+    After training, one of the food rewards is devalued through selective satiation.
+    Then, during testing, the same training task with the same stimulus-response-outcome is repeated without a reward.
+
+    Two groups have been considered:
+    \begin{descriptionlist}
+        \item[1-day group] with little training.
+        \item[3-day group] with extensive training.
+    \end{descriptionlist}
+
+    Behavioral results show that:
+    \begin{itemize}
+        \item Before devaluation, there were no significant differences between the responses of the two groups independently of the type of food.
+        \item During testing, the 1-day group showed a goal-directed behavior while the 3-day group showed a habitual behavior.
+    \end{itemize}
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.55\linewidth]{./img/human_habitual_experiment2.png}
+    \end{figure}
+
+    During both training and testing, the fRMIs of the candidates were taken.
+    Neural results show that, in the 3-day group, the \textbf{dorsolateral striatum} had significant activity.
+\end{casestudy}
+
+
+\subsection{Generation 3} \label{sec:instrumental_gen3}
+
+Formalized goal-directed and habitual actions:
+
+\begin{descriptionlist}
+    \item[Model-based] (Goal-directed) \marginnote{Model-based}
+        Use a model to predict the consequences of actions in terms of future states and expected rewards from future states.
+    
+        When the environment changes, the agent can update its policy of future states without the need to actually be in those states.
+
+    \item[Model-free] (Habitual) \marginnote{Model-free}
+        Select actions based on the stored state-action pairs learned over many trials.
+
+        When the environment changes, the agent has to move into the new states and experience them.
+
+    \item[Hybrid model] \marginnote{Hybrid model}
+        Integrated computational and neural architecture where 
+        model-based and model-free systems act simultaneously and competitively.
+
+        This is the currently favored model for behavior.
+\end{descriptionlist}
+
+
+\begin{casestudy}[Latent learning in humans]
+    The experiment consists of a sequential two-choice Markov decision task in which candidates navigate a binary decision tree.
+
+    Each state contains a fractal image and
+    candidates can choose to move to the left or right branch, each of which will lead with probability $0.7/0.3$ to one of the two subsequent states.
+    When a leaf is reached, a monetary reward (0\textcentoldstyle, 10\textcentoldstyle\, or 25\textcentoldstyle) is delivered.
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.4\linewidth]{./img/human_latent_experiment.png}
+    \end{figure}
+
+    \begin{minipage}{0.58\linewidth}
+        The experiment is divided into two sessions:
+        \begin{descriptionlist}
+            \item[First session]
+                Candidates choices are fixed but they can learn the transition probabilities.
+    
+            \item[Before second session]
+                Candidates are presented with the association between fractal and reward.
+    
+            \item[Second session]
+                Candidates are free to choose their actions at each state.
+        \end{descriptionlist}
+    \end{minipage}
+    \begin{minipage}{0.4\linewidth}
+        \centering
+        \includegraphics[width=0.95\linewidth]{./img/human_latent_experiment2.png}
+    \end{minipage}\\[1em]
+
+    \begin{minipage}{0.6\linewidth}
+        Behavioral results show that the majority of the candidates are able to make the optimal choice.
+        This indicates that their behavior cannot be explained using a model-free learning theory (as learning only happens with a reward).
+        A hybrid model has been proposed to model the candidates' behavior. It includes:
+        \begin{descriptionlist}
+            \item[Reward prediction error] Associated to model-free learning.
+            \item[State prediction error] Associated to model-based learning.
+        \end{descriptionlist}
+    \end{minipage}
+    \begin{minipage}{0.4\linewidth}
+        \centering
+        \includegraphics[width=0.7\linewidth]{./img/human_latent_experiment3.png}
+    \end{minipage}\\[1em]
+
+    On a neuronal level, fRMIs show that:
+    \begin{itemize}
+        \item State prediction error activates the \textbf{intraparietal sulcus} and the \textbf{lateral prefrontal cortex}.
+        \item Reward prediction error activates the \textbf{ventral striatum}.
+    \end{itemize}
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.75\linewidth]{./img/human_latent_experiment4.png}
+    \end{figure}
+\end{casestudy}
+
+\begin{casestudy}[Model-free vs model-based in humans]
+    Consider a Markov decision task that works as follows:
+    \begin{itemize}
+        \item In the first stage, candidates have to choose between two fractal images, 
+            each leading to one of the two subsequent states with probability $0.7$ (common) and $0.3$ (rare).
+        \item In the second stage, candidates have to choose between two fractal images, 
+            each of which will lead to a monetary reward with a certain independent probability.
+        \item The probability of receiving the reward changes stochastically during the trials.
+    \end{itemize}
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.35\linewidth]{./img/model_free_based_theoretical.png}
+    \end{figure}
+
+    It is expected that:
+    \begin{descriptionlist}
+        \item[Model-free agents] 
+            Ignore the transition structure and prefer to repeat actions that lead to a reward in the past.
+
+        \item[Model-based agents] 
+            Respect the transition structure and modify their policies depending on the outcome.
+
+            They are more likely to repeat an action following a rewarding trial only if that transition is common.
+    \end{descriptionlist}
+
+    Despite that, the actual results on human candidates show that a hybrid model is more suited to explain human behavior.
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.6\linewidth]{./img/human_hybrid_model.png}
+    \end{figure}
+
+    % \begin{figure}[H]
+    %     \centering
+    %     \includegraphics[width=0.5\linewidth]{./img/model_free_based_theoretical2.png}
+    % \end{figure}
+
+    Neural results from fRMIs also show that the activity in the \textbf{striatum} increases for both model-based and model-free prediction errors.
+\end{casestudy}

src/year1/cognition-and-neuroscience/module1/sections/_introduction.tex

@@ -0,0 +1,133 @@
+\chapter{Introduction}
+
+
+
+\section{Definitions}
+
+\begin{description}
+    \item[Neuroscience] \marginnote{Neuroscience}
+        Study of the nervous system (structure aspects) on various levels of detail:
+        \begin{descriptionlist}
+            \item[Molecular] Proteins and molecular signaling of the nervous system.
+            \item[Cellular] Morphological and physiological properties of neurons.
+            \item[Neural system] Creation and functioning of networks of neurons.
+        \end{descriptionlist}
+
+
+    \item[Cognition] \marginnote{Cognition}
+        Mental processes (function aspects) that react to inputs. 
+        It involves processes regarding the acquisition, storage, manipulation, and retrieval of information.
+        \begin{descriptionlist}
+            \item[Perception] Information from the environment.
+            \item[Attention] Focus on a specific stimulus in the environment.
+            \item[Learning] Merging new information with prior knowledge.
+            \item[Memory] Encoding, storing, and retrieving information.
+            \item[Action] Interact with the environment using perceived information.
+            \item[Language] Understanding and producing spoken or written thoughts.
+            \item[Higher reasoning] Decision-making and problem-solving.
+        \end{descriptionlist}
+
+
+    \item[Biomimicry] \marginnote{Biomimicry}
+        Solving problems by taking inspiration from elements of nature.
+
+        As proof of general intelligence\footnote{\includegraphics[width=1cm]{img/doubt.png}}, 
+        the human brain is taken as the model for artificial intelligence.
+        Moreover, a successful brain-inspired AI application can 
+        provide a possibly plausible explanation of the functioning of the brain.
+        
+        However, a brain differs from a computer in many aspects:
+        \begin{itemize}
+            \item Hardware and software are distinct while mind and brain are not.
+            \item Machines learn by exploiting the capability of using a large memory
+                while brains have limited capacity but high generalization ability.
+            \item Brains produce both electrical and biochemical signals and 
+                have feedforward, feedback, and recurrent connections
+                while machines typically only employ feedforward connections.
+        \end{itemize}
+
+        \begin{description}
+            \item[Structure emulation] 
+                Mimic or reverse engineer the structure of the brain (e.g. Blue Brain Project).
+
+            \item[Function emulation] 
+                Mimic a neural system on the algorithmic level (e.g. Deep Mind).
+        \end{description}
+
+
+    \item[Cognitive neuroscience] \marginnote{Cognitive neuroscience}
+        Study of the relationship between the physical brain and the intangible mind (thoughts, ideas).
+        In other words, it studies the relationship between structure and function.
+
+        \begin{casestudy}[Severed Corpus Callosum \href{https://www.youtube.com/watch?v=lfGwsAdS9Dc}{\texttt{video}}]
+            Normally, the right and left hemispheres of the brain can communicate. 
+            Moreover, the left visual field is sent to the right hemisphere and 
+            the right visual field is sent to the left hemisphere.
+
+            In patients where the hemispheres are split, a text shown on the right visual side is recognized as 
+            the speech capabilities are located in the left hemisphere,
+            while a text shown on the left visual side does not trigger any speech reaction.
+        \end{casestudy}
+\end{description}
+
+
+
+\section{Neuroscience history}
+
+Two main schools of thought emerged and are still the subject of ongoing debates:
+\begin{descriptionlist}
+    \item[Localizationism] \marginnote{Localizationism}
+        Specific regions of the brain are responsible for particular faculties.
+
+        Assuming localizationism, 52 distinct regions with different neurons can be identified.
+
+    \item[Aggregate field theory] \marginnote{Aggregate field theory}
+        The brain works as a whole for mental functions.
+\end{descriptionlist}
+
+
+\subsection{Neuron doctrine}
+\marginnote{Neuron doctrine}
+The nervous system is made of a discrete amount of individual neurons (and not a continuous tissue).
+
+\begin{description}
+    \item[Principle of dynamic polarization] 
+        Electrical signals in a neuron flow only in a single direction.
+
+    \item[Principle of connectional specificity] 
+        Neurons do not connect randomly but make specific connections at particular contact points.
+
+    \item[Synapse] \marginnote{Synapse}
+        Point of contact of two neurons. A synapse can be chemical or electrical.
+\end{description}
+
+
+
+\section{Cognitive science history}
+
+\begin{description}
+    \item[Rationalism] \marginnote{Rationalism}
+        All knowledge can be derived through reasoning, without sensory experiences.
+
+    \item[Empiricism] \marginnote{Empiricism}
+        The brain starts as a blank slate and knowledge is added through sensory experiences.
+
+    \item[Associationism] \marginnote{Associationism}
+        Inspired by empiricism.
+        Learning happens by correlating individual experiences (e.g. actions followed by a reward will be repeated).
+
+    \item[Behaviorism] \marginnote{Behaviorism}
+        Inspired by empiricism. 
+        Everyone has the same neural basis that is improved through learning.
+        Learning only involves observable behaviors.
+\end{description}
+
+\begin{remark}
+    Associationism and behaviorism are not able to explain all types of learning (e.g. language).
+\end{remark}
+
+\begin{description}
+    \item[Cognitivism] \marginnote{Cognitivism}
+        The psychological and biological levels of an individual cannot be separated.
+        Learning is based on the biology of the neurons.
+\end{description}

src/year1/cognition-and-neuroscience/module1/sections/_nervous_system.tex

@@ -0,0 +1,777 @@
+\chapter{Nervous system anatomy and physiology}
+
+
+\begin{description}
+    \item[Central nervous system] Brain and spinal cord.
+    \item[Peripheral nervous system] Nerves that branch off from the brain and the spine.
+\end{description}
+
+\section{Individual cells}
+
+% A nervous system has two types of cells:
+% \begin{descriptionlist}
+%     \item[Neurons/nerve cells] 
+%     \item[Glia cells/neuroglia] 
+% \end{descriptionlist}
+
+
+\subsection{Glia cells / Neuroglia}
+\marginnote{Glia cells/Neuroglia}
+Cells that support neurons.
+There are 2 to 10 times more glia cells than neurons.\\
+
+\begin{minipage}{0.89\textwidth}
+    \begin{descriptionlist}
+        \item[Microglia] \marginnote{Microglia}
+            Immune system cells located in the central nervous system.
+            They intervene in response to toxic agents or to clear dead cells.
+            \begin{itemize}
+                \item Responsible for antigen presentation (determine the type of external agent).
+                \item Become phagocytes (cells that ingest harmful agents) during injuries, infections, or degenerative diseases.
+            \end{itemize}
+    
+            \begin{remark}
+                In patients affected by Alzheimer's disease, microglia may become hyperactive and damage neurons.
+            \end{remark}
+    \end{descriptionlist}
+\end{minipage}
+\begin{minipage}{0.1\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{./img/microglia.png}
+\end{minipage}\\[1em]
+
+\begin{minipage}{0.79\textwidth}
+    \begin{descriptionlist}
+        \item[Astrocytes] \marginnote{Astrocytes}
+            Star-shaped cells located in the central nervous system.
+            They surround neurons and are in contact with the brain's vasculature.
+            \begin{itemize}
+                \item Provide nourishment to neurons.
+                \item Regulate the concentration of ions and neurotransmitters in the extracellular space.
+                \item Communicate with the neurons to modulate synaptic signaling.
+                \item Maintain the blood-brain barrier that separates the tissues of the central nervous system and the blood.
+            \end{itemize}
+    \end{descriptionlist}
+\end{minipage}
+\begin{minipage}{0.2\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{./img/astrocyte.png}
+\end{minipage}\\[1em]
+
+\begin{minipage}{0.82\textwidth}
+    \begin{descriptionlist}
+        \item[Oligodendrocytes and Schwann cells] \marginnote{Oligodendrocytes\\Schwann cells}
+            Oligodendrocytes are located in the central nervous system, while 
+            Schwann cells are located in the peripheral nervous system.
+            \begin{itemize}
+                \item Produce thin sheets of myelin that wrap concentrically around the axon of the neurons.
+                    This insulating material allows the rapid conduction of electrical signals along the axon.
+            \end{itemize}
+
+            \begin{remark}
+                Myelin is white, giving the name to the white matter.
+            \end{remark}
+
+            \begin{remark}
+                In multiple sclerosis, the immune system attacks the oligodendrocytes, 
+                slowing or disrupting messages traveling along the nerves.
+            \end{remark}
+    \end{descriptionlist}
+\end{minipage}
+\begin{minipage}{0.17\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{./img/insulation.png}
+\end{minipage}
+
+
+
+\subsection{Neurons / Nerve cells}
+\marginnote{Neurons/Nerve cells}
+
+A nervous system has around 100 billion neurons.
+There are 100 distinct types of neurons varying in form, location, and interconnectivity.
+
+Generally, a neuron does the following:
+\begin{enumerate}
+    \item Receives some information.
+    \item Makes a decision.
+    \item Passes it to other neurons.
+\end{enumerate}
+
+\begin{description}
+    \item[Eukaryotic cell] \marginnote{Eukaryotic cell}
+        A neuron is an eukaryotic cell. Therefore, it has:
+        \begin{description}
+            \item[Cell membrane] Membrane that separates the intracellular and extracellular space.
+            \item[Cytoplasm] Intracellular fluid mainly made of proteins and ions of potassium, sodium, chloride, and calcium.
+            \item[Extracellular fluid] Fluid in which the neuron sits. Similar composition of the cytoplasm.
+            \item[Cell body/soma] Metabolic center of the cell.
+        \end{description}
+
+        \begin{figure}[H]
+            \centering
+            \includegraphics[width=0.5\textwidth]{img/neuron_eukaryotic.png}
+            \caption{Neuron as an eukaryotic cell}
+        \end{figure}
+\end{description}
+
+\begin{description}
+    \item[Neuron-specific components] \phantom{}
+        \begin{description}
+            \item[Dendrites] \marginnote{Dendrites}
+                Receives the outputs of other neurons.
+                A neuron has multiple dendrites with different shapes depending on the type and location of the neuron.
+            \item[Axon] \marginnote{Axon}
+                Transmitting zone of the neuron that carries electrical signals from the dendrites to the synapses (from 0.1mm to 2m).
+                A neuron has a single axon.
+            \item[Synapses] \marginnote{Synapses}
+                Represents the output zone of the neuron from where electrical or chemical signals can be transmitted to other cells.
+                A neuron has multiple synapses.
+
+                \begin{description}
+                    \item[Presynaptic cell] Cell transmitting a signal.
+                    \item[Postsynaptic cell] Cell receiving a signal.
+                    \item[Synaptic cleft] Narrow space separating presynaptic and postsynaptic cells (i.e. the space separating two neurons).
+                \end{description}
+        \end{description}
+\end{description}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.9\textwidth]{img/neuron_specific.png}
+    \caption{Neuron-specific components}
+\end{figure}
+
+There are three types of synapses:
+\begin{descriptionlist}
+    \item[Axosomatic] \marginnote{Axosomatic}
+        Synapses that a neuron makes onto the cell body (soma) of another neuron.
+    \item[Axodendritic] \marginnote{Axodendritic}
+        Synapses that a neuron makes onto the dendrites of another neuron.
+    \item[Axoaxonic] \marginnote{Axoaxonic}
+        Synapses that a neuron makes onto the synapses of another neuron.
+        In this case, the transmitting neuron can be seen as a signal modulator of the receiving neuron.
+    \begin{figure}[H]
+        \begin{subfigure}{.3\textwidth}
+            \centering
+            \includegraphics[width=\linewidth]{./img/axosomatic.png}
+            \caption{Axosomatic}
+        \end{subfigure}
+        \begin{subfigure}{.3\textwidth}
+            \centering
+            \includegraphics[width=\linewidth]{./img/axodendritic.png}
+            \caption{Axodendritic}
+        \end{subfigure}
+        \begin{subfigure}{.3\textwidth}
+            \centering
+            \includegraphics[width=\linewidth]{./img/axoaxonic.png}
+            \caption{Axoaxonic}
+        \end{subfigure}
+    \end{figure}
+\end{descriptionlist}
+
+Neurons are divided into three functional categories:
+\begin{descriptionlist}
+    \item[Sensory neurons] \marginnote{Sensory neurons}
+        Carry information from the body's peripheral sensors into the nervous system.
+        Provides both perception and motor coordination.
+
+    \item[Motor neurons] \marginnote{Motor neurons}
+        Carry commands from the brain or the spinal cord to muscles and glands.
+
+    \item[Interneurons] \marginnote{Interneurons}
+        Intermediate neurons between sensory and motor neurons.
+\end{descriptionlist}
+
+\begin{description}
+    \item[Principle of connectional specificity] \marginnote{Principle of connectional specificity}
+        Neurons do not connect randomly but rather make specific connections at particular contact points.
+\end{description}
+
+
+
+\section{Information transfer within a neuron}
+
+
+\subsection{Neuron functional regions}
+
+In a neuron, there are four regions that handle signals:
+\begin{descriptionlist}
+    \item[Input zone] \marginnote{Input zone}
+        Dendrites collect information from different sources
+        in the form of \aclp{psp} (\acp{psp}).
+
+    \item[Integration/trigger zone] \marginnote{Integration/trigger zone}
+        \acp{psp} are summed at the axon hillock and an \ac{ap} is generated if a threshold (-55mV) has been exceeded.
+
+    \item[Conductive zone] \marginnote{Conductive zone}
+        The \ac{ap} is propagated through the axon.
+
+    \item[Output zone] \marginnote{Output zone}
+        Synapses transfer information to other cells.
+
+        \begin{description}
+            \item[Chemical synapses] The frequency of \acp{ap} determines the amount of neurotransmitters released.
+            \item[Electrical synapses] The \ac{ap} is directly transmitted to the next neurons.
+        \end{description}
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.8\textwidth]{./img/neuron_transmission.png}
+        \caption{Transmitting regions of different types of neurons}
+    \end{figure}
+\end{descriptionlist}
+
+
+\subsection{Neuron transmission signals}
+
+\begin{description}
+    \item[Resting membrane potential] \marginnote{Resting membrane potential}
+        In a resting neuron, the voltage inside the cell is more negative ($-70$mV) than the outside.
+        This allows the creation of an electrical signal when needed.
+
+    \item[\Acl{psp} (\ac{psp})] \marginnote{\Acl{psp} (\ac{psp})}
+        Small change in the membrane potential that alters the resting voltage of the cell.
+        
+        A \ac{psp} can be:
+        \begin{descriptionlist}
+            \item[Excitatory \ac{psp} (\acs{epsp})] \marginnote{Excitatory \ac{psp}}
+                Has a depolarizing role: produces a decrease in the membrane potential (i.e. increases voltage inside the cell), 
+                therefore enhancing the ability to generate an \ac{ap}.
+
+            \item[Inhibitory \ac{psp} (\acs{ipsp})] \marginnote{Inhibitory \ac{psp}}
+                Has a hyperpolarizing role: produces an increase in the membrane potential (i.e. reduces voltage inside the cell), 
+                therefore reducing the ability to generate an \ac{ap}.
+        \end{descriptionlist}
+
+        A \ac{psp} has the following properties:
+        \begin{itemize}
+            \item The amplitude and duration of the signal are determined by the size of the stimulus that caused it.
+                Overall, the amplitude is small.
+            \item The signal is passively conducted through the cytoplasm, therefore it decays with distance and is able to travel 1mm at most.
+            \item A single \acs{epsp} is not enough to fire a neuron. Multiple \acp{psp} are summed at the axon hillock.
+                There are two types of summation:
+                \begin{descriptionlist}
+                    \item[Spatial summation] Sum of the \acp{psp} received at the same time.
+                    \item[Temporal summation] Sum of the \acp{psp} received at different time points.
+                \end{descriptionlist}
+
+                \begin{remark}
+                    The fact that a single \ac{epsp} is not enough to fire a neuron prevents a response to every single stimulus.
+                \end{remark}
+        \end{itemize}
+
+    \item[\Acl{ap} (\ac{ap})] \marginnote{\Acl{ap} (\ac{ap})}
+        Signal generated when the sum of \acp{epsp} exceeds a fixed threshold of $-55$mV (all-or-none).
+
+        \begin{description}
+            \item[Saltatory conduction] \marginnote{Saltatory conduction}
+                Mechanism that allows a fast propagation on long distances of \acp{ap}.
+                \begin{enumerate}
+                    \item Depolarization causes the sodium ion (Na+) channels located in the nodes of Ranvier of the axon to gradually open.
+                    \item Na+ flows into the neuron and further depolarizes it until the Na+ equilibrium potential is reached.
+                    \item With Na+ equilibrium, Na+ channels close and potassium ion (K+) channels open.
+                    \item K+ flows into the neuron and restores the membrane potential until the K+ equilibrium potential is reached.
+                    \item With K+ equilibrium, K+ channels close and 
+                        the membrane potential of the neuron is more negative than the resting potential (hyperpolarization).
+                        It will gradually return to its resting potential.
+                        \begin{remark}
+                            During hyperpolarization, Na+ channels cannot open (refractory period).
+                            This has two implications:
+                            \begin{itemize}
+                                \item It limits the number of times a neuron can fire in a given time.
+                                \item Guarantees a unidirectional electrical current flow 
+                                    (\textbf{Principle of dynamic polarization}).\marginnote{Principle of dynamic polarization} 
+                            \end{itemize}
+                        \end{remark}
+                \end{enumerate}
+
+                \begin{figure}[H]
+                    \centering
+                    \includegraphics[width=0.8\textwidth]{./img/neuron_transmission2.png}
+                    \caption{
+                        \parbox[t]{0.6\linewidth}{
+                            Signal from the input to the output zone. The amplitude of the stimulus modulates the frequency of \ac{ap}.
+                        }
+                    }
+                \end{figure}
+
+                \begin{figure}[H]
+                    \begin{subfigure}{.45\textwidth}
+                        \centering
+                        \includegraphics[width=0.85\textwidth]{./img/saltatory_conduction.png}
+                        \caption{Ion channels along the axon}
+                    \end{subfigure}
+                    \begin{subfigure}{.45\textwidth}
+                        \centering
+                        \includegraphics[width=0.8\textwidth]{./img/action_potential.png}
+                        \caption{Triggering of an action potential}
+                    \end{subfigure}
+                \end{figure}
+        \end{description}
+
+
+        \begin{remark}
+            As the signal is constantly regenerated, 
+            \Acp{ap} have similar amplitude and duration in all neurons, regardless of the characteristics of the input \acp{psp}.
+            Therefore, the only way an \ac{ap} has to carry information is by varying frequency depending on the stimulus intensity, making it a binary signal.
+        \end{remark}
+\end{description}
+
+\begin{example}
+    Seizures are caused by misfiring neurons.
+\end{example}
+
+
+
+\section{Information transfer between two neurons}
+
+
+\subsection{Electrical synapse}
+
+\begin{minipage}{0.55\textwidth}
+    \begin{description}
+        \item[Structure] \marginnote{Electrical synapse}
+            The neuronal membranes of the presynaptic and postsynaptic neurons are in contact at \textbf{gap junctions} and
+            the cytoplasm of the two neurons is virtually continuous through connecting \textbf{pores}.
+    \end{description}
+\end{minipage}
+\begin{minipage}{0.35\textwidth}
+    \centering
+    \includegraphics[width=\linewidth]{./img/electric_synapse.png}
+\end{minipage}
+
+\begin{description}
+    \item[Functioning]
+        The two neurons are \textbf{isopotential} (i.e. they have the same membrane potential) and 
+        the ions of the presynaptic neurons are instantaneously transmitted to the postsynaptic neuron.
+
+    \item[Properties] \phantom{}
+        \begin{itemize}
+            \item Fast transmission.
+            \item Allows for synchronous operations involving groups of neurons.
+            \item The strength of the signal cannot be modulated.
+        \end{itemize}
+\end{description}
+
+
+\subsection{Chemical synapse}
+
+\begin{description}
+    \item[Structure] \marginnote{Chemical synapse}
+        The synaptic cleft separates the presynaptic and postsynaptic neurons.
+        \begin{description}
+            \item[Neurotransmitter] 
+                Chemical substance received by the receptors of the postsynaptic neuron.
+
+                The effect of a neurotransmitter is decided by the receiving receptor and not by the cell transmitting it.
+
+            \item[Presynaptic terminals] 
+                Swellings at the end of the axon that contain synaptic vesicles.
+
+            \item[Synaptic vesicles] 
+                Vesicles containing neurotransmitter molecules.
+        \end{description}
+
+    \item[Functioning] 
+        The release of neurotransmitter molecules is based on the following steps:
+        \begin{enumerate}
+            \item An action potential arriving at the terminal of a presynaptic axon causes the calcium ion (Ca$^{2+}$) voltage-gates to open.
+            \item Ca$^{2+}$ flow into the cell and 
+                cause the synaptic vesicles to bind to the cell membrane to release neurotransmitters into the synaptic cleft.
+            \item Neurotransmitters cross the synaptic cleft and bind to the receptors of the postsynaptic neuron.
+                Depending on the neurotransmitter and the receiving receptor, there might be a generation of \ac{epsp} or \ac{ipsp}.
+        \end{enumerate}
+
+        \begin{center}
+            \includegraphics[width=0.9\linewidth]{./img/chemical_synapse.png}
+        \end{center}
+
+        When a receptor recognizes the neurotransmitter, it is released back into the synaptic cleft.
+        To avoid a constant stimulation of the receptors, neurotransmitters are inactivated:
+        \begin{itemize}
+            \item The synaptic terminal can reuptake neurotransmitters through transporter proteins.
+            \item Neurotransmitters might degenerate or be broken down by special enzymes.
+            \item Neurotransmitters can be released far away from the site of the receptors.
+        \end{itemize}
+
+    \item[Properties] \phantom{}
+        \begin{itemize}
+            \item Slow transmission.
+            \item The signal can be modulated.
+            \item Has specific effects depending on the neurotransmitter and the receptors.
+        \end{itemize}
+\end{description}
+
+
+
+\section{Neural circuit}
+
+\begin{description}
+    \item[Neural circuit] \marginnote{Neural circuit}
+        Group of interconnected neurons that process a specific kind of information.
+
+        \begin{remark}
+            The behavioral function of each neuron is determined by its connections.
+        \end{remark}
+
+    \item[Types of neurons] \phantom{}
+        \begin{description}
+            \item[Sensory neuron] \marginnote{Sensory neuron}
+                Carry information from the peripheral sensors to the nervous system for both perception and motor coordination.
+        
+            \item[Motor neuron] \marginnote{Motor neuron}
+                Carry information from the nervous system to muscles and glands.
+            
+            \item[Interneuron] \marginnote{Interneuron}
+                Intermediate neurons between sensory and motor neurons.
+        \end{description}
+\end{description}
+
+\begin{remark}
+    In vertebrates, a stimulus causes multiple neural pathways to simultaneously encode different information.
+    This allows for parallel processing to increase both the speed and reliability of the information transfer.
+\end{remark}
+
+\begin{description}
+    \item[Neural pathways types] \phantom{}
+        \begin{description}
+            \item[Divergent pathway] \marginnote{Divergent pathway}
+                One neuron activates many target cells.
+                Typically happens at the input stages of the nervous system
+                to ensure that a single neuron has a wide and diverse influence.
+
+            \item[Convergent pathway] \marginnote{Convergent pathway}
+                Many neurons activate a single target cell.
+                Typically happens at the output stages of the nervous system 
+                to ensure that a motor neuron is activated only when a sufficient number of neurons are firing.
+        \end{description}
+
+    \item[Neuron firing types] \phantom{}
+        \begin{description}
+            \item[Excitatory neuron] \marginnote{Excitatory neuron}
+                Neurons that produce signals that increase the probability of firing of the postsynaptic neurons.
+                
+            \item[Inhibitory neuron] \marginnote{Inhibitory neuron}
+                Neurons that produce signals that decrease the probability of firing of the postsynaptic neurons.
+                
+                \begin{description}
+                    \item[Feed-forward inhibition] 
+                        Excitatory neurons connected to inhibitory interneurons to block other downstream neurons.
+                        Allows to enhance the active pathway and to block other antagonist actions.
+                        \begin{figure}[H]
+                            \centering
+                            \includegraphics[width=0.4\textwidth]{./img/feedforward_inhibition.png}
+                            \caption{Example of feed-forward inhibition}
+                        \end{figure}
+
+                    \item[Feed-back inhibition] 
+                        Excitatory neurons connected to inhibitory interneurons that return to the same neurons to inhibit them.
+                        Prevents the overload of neurons or muscles.
+                        \begin{figure}[H]
+                            \centering
+                            \includegraphics[width=0.4\textwidth]{./img/feedback_inhibition.png}
+                            \caption{Example of feed-back inhibition}
+                        \end{figure}
+                \end{description}
+        \end{description}
+\end{description}
+
+
+
+\begin{casestudy}[Knee-jerk reflex]
+    By tapping the patellar tendon (below the kneecap), the following happens:
+    \begin{enumerate}
+        \item The sensory information is conveyed from the muscle to the spinal cord (central nervous system).
+        \item The nervous system issues motor commands to the muscles which results in the knee jerk.
+        \item Inhibitory commands are issued to stop antagonist muscles.
+    \end{enumerate}
+
+    \begin{center}
+        \includegraphics[width=0.8\textwidth]{./img/knee_jerk.png}
+    \end{center}
+\end{casestudy}
+
+
+\section{Neural system}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.3\textwidth]{./img/neural_system.png}
+    \caption{Composition of the nervous system}
+\end{figure}
+
+
+\subsection{\Acl{pns} (\acs{pns})}
+
+The \acl{pns} is composed of:
+\begin{descriptionlist}
+    \item[Nerves] \marginnote{Nerves}
+        Groups of axons and glia.
+
+    \item[Ganglia] \marginnote{Ganglia}
+        Groups of neuron bodies outside the \acl{cns}
+\end{descriptionlist}
+
+The \ac{pns} has the following functions:
+\begin{itemize}
+    \item Delivers sensory information to the \acl{cns}.
+    \item Carries commands from the \acl{cns} to the muscles.
+    \item Supplies the \acl{cns} with information regarding both the external and internal environment.
+\end{itemize}
+
+The \ac{pns} has the following divisions:
+\begin{descriptionlist}
+    \item[Somatic nervous system] \marginnote{Somatic nervous system} \phantom{}
+        \begin{itemize}
+            \item Sensory neurons that receive information from the skin, muscles, and joints.
+            \item Converts perceived spatial and physical information into electrical signals for the \acl{cns} to process.
+            \item Controls the voluntary muscles.
+        \end{itemize}
+
+    \item[Autonomic nervous system] \marginnote{Autonomic nervous system} \phantom{}
+        \begin{itemize}
+            \item Controls internal organs (viscera), the vascular system, and involuntary muscles and glands.
+            \item Divided into three systems:
+                \begin{descriptionlist}
+                    \item[Sympathetic system] \marginnote{Sympathetic system}
+                        Operates antagonistically against the parasympathetic system.
+                        Handles the body's response to stress (using norepinephrine).
+
+                        Physically, the sympathetic system originates from the spinal cord. 
+                        Its ganglia are closer to the spinal cord,
+                        therefore the axons from the \acl{cns} to the ganglia are shorter than the axons from the ganglia to the organs.
+
+                        \begin{example}
+                            Stimulates adrenal glands to prepare the body for action (fight or flight),
+                            increases heart rate,
+                            diverts the blood from the digestive tract to the somatic musculature, \dots
+                        \end{example}
+
+                    \item[Parasympathetic system] \marginnote{Parasympathetic system}
+                        Operates antagonistically against the sympathetic system.
+                        Acts to preserve the body's resources and restore homeostasis (using acetylcholine).
+
+                        Physically, the parasympathetic system originates from the base of the brain and from the sacral spinal cord.
+                        Its ganglia are outside the spinal cord, sometimes inside the affected organs,
+                        therefore the axons from the \acl{cns} to the ganglia are longer than the axons from the ganglia to the organs.
+
+                        \begin{example}
+                            Slows heart rate, stimulates digestion, \dots
+                        \end{example}
+
+                    \item[Enteric system] \marginnote{Enteric system}
+                        Controls the involuntary muscles of the gut.
+                \end{descriptionlist}
+        \end{itemize}
+\end{descriptionlist}
+
+
+
+\subsection{\Acl{cns} (\acs{cns})}
+
+\begin{description}
+    \item[Meninges] \marginnote{Meninges}
+        Three layers of membrane protecting the brain and the spinal cord.
+        \begin{descriptionlist}
+            \item[Dura mater] The outermost and thickest layer.
+            \item[Arachnoid mater] The middle layer.
+            \item[Pia mater] The innermost and most delicate layer. It adheres to the brain's surface.
+        \end{descriptionlist}
+
+    \item[Cerebrospinal fluid] \marginnote{Cerebrospinal fluid}
+        Fluid that allows the brain to float and prevents it from simply sitting on the skull surface.
+        It also reduces the shock to the brain and the spinal cord in case of rapid accelerations/decelerations.
+
+        The fluid is located in:
+        \begin{itemize}
+            \item The space between the arachnoid mater and the pia mater.
+            \item The brain ventricles.
+            \item Cisterns and sulcis.
+            \item The central canal of the spinal cord.
+        \end{itemize}
+
+    \item[Blood-brain barrier] \marginnote{Blood-brain barrier}
+        Barrier between the brain's capillaries and the brain's tissue.
+        It protects against pathogens and toxins.
+
+        \begin{remark}
+            The effectiveness of the barrier also prevents drugs to treat mental and neurological disorders from passing through.
+        \end{remark}
+
+    \item[Spinal cord] \marginnote{Spinal cord}
+        Acts as a relay for the information coming in and out of the brain.
+        It is enclosed in the vertebral column.
+\end{description}
+
+\begin{remark}
+    Most pathways in the \ac{cns} are bilaterally symmetrical: 
+    the sensory and motor activities of one side of the body are handled by the cerebral hemisphere on the opposite side.
+\end{remark}
+
+\begin{description}
+    \item[Brain] \marginnote{Brain}
+    
+    \begin{minipage}{0.6\textwidth}
+        \begin{description}
+            \item[Brain stem] \marginnote{Brain stem}
+                Regulates basic life functions such as blood pressure, respiration, and sleep/wakefulness.
+                It is divided into three sections:
+                \begin{itemize}
+                    \item Medulla.
+                    \item Pons.
+                    \item Midbrain.
+                \end{itemize}
+        \end{description}
+    \end{minipage}
+    \begin{minipage}{0.35\textwidth}
+        \centering
+        \includegraphics[width=\linewidth]{./img/brain_sections.png}
+    \end{minipage}
+
+    \begin{description}
+        \item[Cerebellum] \marginnote{Cerebellum}
+            Contains lots of neurons and is responsible for:
+            \begin{itemize}
+                \item Maintaining posture.
+                \item Coordinating head, eye, and arm movement.
+                \item Regulating motor control (i.e. adjustments to the movement).
+                \item Learning motor skills.
+            \end{itemize}
+
+        \item[Diencephalon] \marginnote{Diencephalon} 
+            \phantom{}\\
+            \begin{minipage}{0.6\linewidth}
+                \begin{description}
+                    \item[Thalamus] \marginnote{Thalamus}
+                        Sorts incoming sensory information (except the sense of smell) of the \acl{pns} and 
+                        sends them to the sensory regions of the cerebral hemispheres.
+                    \item[Hypothalamus] \marginnote{Hypothalamus}
+                        Regulates the autonomic nervous system and homeostasis through the pituitary gland (which releases hormones).
+                        Handles the motivation system of the brain by favoring behaviors the organism finds rewarding.
+                \end{description}
+            \end{minipage}
+            \begin{minipage}{0.35\linewidth}
+                \centering
+                \includegraphics[width=\linewidth]{./img/diencephalon.png}
+            \end{minipage}
+
+        \item[Telencephalon/Cerebral hemispheres] \marginnote{Telencephalon/Cerebral hemispheres}
+            Consists of:
+            \begin{description}
+                \item[Cerebral cortex] 
+                    Made of gray matter (body of neurons).
+
+                \item[White matter] 
+                    (axons and glial cells).
+
+                \item[Basal ganglia] \marginnote{Basal ganglia}
+                    Receive inputs from sensory and motor areas and 
+                    mostly send them through the thalamus to the frontal lobe.
+
+                    They have a crucial role in motor control and reinforcement learning.
+                    This happens through two pathways:
+                    \begin{descriptionlist}
+                        \item[Direct pathway] When active, it causes the disinhibition of the thalamus and has the consequence of initializing movement.
+                        \item[Indirect pathway] When active, it causes the inhibition of the thalamus and consequently inhibits movement.
+                    \end{descriptionlist}
+                    To activate the direct pathway and inhibit the indirect pathway, the substantia nigra pars compacta (SNc) releases the neurotransmitter dopamine.
+
+                    \begin{example}[Parkinson's disease]
+                        In patients affected by Parkinson's disease, the dopamine-related neurons in the SNc are lost causing
+                        an overactivation of the indirect pathway that inhibits movement.
+                    \end{example}
+
+                \item[Amygdala] \marginnote{Amygdala}
+                    Responsible for recognizing a stimulus and reacting to it.
+                    Involved in attention, perception, value representation, decision-making, learning, memory, \dots
+
+                \item[Hippocampus] \marginnote{Hippocampus}
+                    Responsible for long-term memory and spatial memory.
+            \end{description}
+
+        \item[Cerebral cortex] \marginnote{Cerebral cortex}
+            Surface of the brain which covers around 2.2m$^2$ to 2.4m$^2$.
+            To cover more surface, the cortex has infoldings (sulci and gyri) which also allow to connect neurons with shorter axons.
+
+            There are two symmetrical hemispheres connected through the corpus callosum and four different lobes.
+
+            \begin{figure}[H]
+                \centering
+                \begin{subfigure}{0.25\linewidth}
+                    \centering
+                    \includegraphics[width=\linewidth]{./img/brain_surface.png}
+                    \caption{Visualization of sulci and gyri}
+                \end{subfigure}
+                \begin{subfigure}{0.35\linewidth}
+                    \centering
+                    \includegraphics[width=\linewidth]{./img/brain_lobes.png}
+                    \caption{Lobes of the brain}
+                \end{subfigure}
+            \end{figure}
+
+            \begin{description}
+                \item[Frontal lobe] \marginnote{Frontal lobe}
+                    \phantom{}
+                    \begin{description}
+                        \item[Motor cortex] \phantom{}
+                            \begin{itemize}
+                                \item Planning and execution of movement.
+                                \item Contains neurons that directly activate somatic movement neurons in the spinal cord.
+                            \end{itemize}
+
+                        \item[Prefrontal cortex] \phantom{}
+                            \begin{itemize}
+                                \item Long-term planning.
+                                \item Decision making.
+                                \item Motivation and value.
+                            \end{itemize}
+                    \end{description}
+                
+
+                \item[Parietal lobe] \marginnote{Parietal lobe}
+                    Receives and integrates information from the outside world, the body, and memory.
+
+                    \begin{description}
+                        \item[Somatosensory cortex]
+                            Receives information regarding touch, pain, temperature, and limb position.
+                    \end{description}
+
+                    \begin{remark}
+                        Neurons responsible for a specific part of the body are clustered together.
+                    \end{remark}
+
+
+                \item[Occipital lobe] \marginnote{Occipital lobe}
+                    \begin{description}
+                        \item[Visual cortex] 
+                            Responsible for vision. 
+                            Encodes features like luminance, spatial frequency, orientation, motion, \dots
+                    \end{description}
+
+                    \begin{remark}
+                        Neurons responsible for processing a specific feature are clustered together.
+                    \end{remark}
+
+
+                \item[Temporal lobe] \marginnote{Temporal lobe}
+                    \begin{description}
+                        \item[Auditory cortex]
+                            Responsible for processing sound.
+                    \end{description}
+
+                    \begin{remark}
+                        Neurons responsible for processing a specific sound frequency are clustered together.
+                    \end{remark}
+
+                \item[Association cortex] \marginnote{Association cortex}
+                    Portion of the cortex that has neither sensory nor motor responsibility.
+                    Receives and integrates inputs from many cortical areas.
+
+                    \begin{description}
+                        \item[Multisensory neuron]
+                            Cell activated by multiple sensory modalities.
+                    \end{description}
+            \end{description}
+    \end{description}
+\end{description}

src/year1/cognition-and-neuroscience/module1/sections/_pavlovian_learning.tex

@@ -0,0 +1,496 @@
+\chapter{Pavlovian/classical learning}
+
+
+Form of prediction learning that aims to learn stimulus-outcome associations:
+\begin{itemize}
+    \item When a reinforcer is likely to occur.
+    \item Which stimuli tend to precede a reinforcer.
+\end{itemize}
+This allows the animal to emit a response in anticipation of a reinforcer.
+
+Pavlovian learning works as follows:\\
+\begin{minipage}{0.58\linewidth}
+    \begin{enumerate}[label=\alph*.]
+        \item A stimulus that has no meaning to the animal will result in \ac{nr}.
+        \item An \ac{us} (i.e. a reinforcer) generates an \ac{ur}.
+        \item Learning happens when a reinforcer is paired with a non-relevant stimulus.
+        \item The learned \ac{cs} generates a \ac{cr}.
+    \end{enumerate}
+\end{minipage}
+\begin{minipage}{0.4\linewidth}
+    \raggedleft
+    \includegraphics[width=0.9\linewidth]{./img/pavlovian_example.png}
+\end{minipage}\\
+
+An outcome can be:
+\begin{descriptionlist}
+    \item[Appetitive] Something considered positive.
+    \item[Aversive] Something considered negative.
+\end{descriptionlist}
+
+The learned \acl{cr} can be:
+\begin{descriptionlist}
+    \item[Behavioral] Associated to the startle response (i.e. reflex in response to a sudden stimulus).
+    \item[Physiological] Associated to the autonomic system.
+    \item[Change in subjective response] 
+\end{descriptionlist}
+
+\begin{remark}
+    Pavlovian learning has its foundations in behaviorism: the brain starts as a blank slate and only observable behaviors can be studied.
+\end{remark}
+
+
+
+\section{Types of reinforcement}
+
+There are two types of learning:
+\begin{descriptionlist}
+    \item[Continuous reinforcement] \marginnote{Continuous reinforcement}
+        The \acl{cs} is reinforced every time the \acl{us} occurs.
+        \begin{remark}
+            More effective to teach a new association.
+        \end{remark}
+
+    \item[Partial reinforcement] \marginnote{Partial reinforcement}
+        The \acl{cs} is not always reinforced.
+        \begin{remark}
+            Learning is slower but the \acl{cr} is more resistant to extinction.
+        \end{remark}
+\end{descriptionlist}
+
+
+
+\section{Learning flexibility}
+
+\begin{description}
+    \item[Acquisition] \marginnote{Acquisition}
+        The probability of occurrence of a \acl{cr} increases if the \acl{cs} is presented with the \acl{us}.
+        
+    \item[Extinction] \marginnote{Extinction}
+        The probability of occurrence of a \acl{cr} decreases if the \acl{cs} is presented alone.
+\end{description}
+
+\begin{remark}
+    Extinction does not imply forgetting.
+    After an association between \ac{cs} and \ac{us} is made, 
+    extinction consists of creating a second association with inhibitory effects that overrides the existing association.
+
+    The extinct association can return in the future
+    (this is more evident when the context is the same as the acquisition phase).
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.95\linewidth]{./img/pavlovian_extinction.png}
+        \caption{Example of acquisition, extinction, and \ac{cr} return}
+    \end{figure}
+\end{remark}
+
+
+\begin{description}
+    \item[Generalization] \marginnote{Generalization} 
+        A new stimulus that is similar to a learned \acl{cs} can elicit a \acl{cr}.
+\end{description}
+
+\begin{casestudy}[Aplysia Californica] \phantom{}\\
+    \begin{minipage}{0.8\linewidth}
+        \begin{enumerate}
+            \item Before conditioning, a stimulus to the siphon of an aplysia californica results in a weak withdrawal of the gill.
+            \item During conditioning, a stimulus to the siphon is paired with a shock to the tail which results in a large withdrawal of the gill.
+            \item After conditioning, a stimulus to the siphon alone results in a large withdrawal response.
+        \end{enumerate}
+    \end{minipage}
+    \begin{minipage}{0.18\linewidth}
+        \centering
+        \includegraphics[width=\linewidth]{./img/aplysia.png}
+    \end{minipage}
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.85\linewidth]{./img/gill_pavlovian.png}
+        \caption{Conditioning process}
+    \end{figure}
+
+    \begin{minipage}{0.55\linewidth}
+        The learned response lasts for days.
+        It can be observed that without training, the response disappears faster.
+    \end{minipage}
+    \begin{minipage}{0.4\linewidth}
+        \begin{figure}[H]
+            \centering
+            \includegraphics[width=0.6\linewidth]{./img/gill_pavlovian_graph.png}
+            \caption{Withdrawal response decay}
+        \end{figure}
+    \end{minipage}
+\end{casestudy}
+
+\begin{remark} \marginnote{Amygdala in Pavlovian learning}
+    In mammals, aversive Pavlovian conditioning involves the amygdala.
+    The \ac{cs} and \ac{us} are relayed from the thalamus and the cerebral cortex to the amygdala, 
+    which in turn connects to various motor responses such as:
+    \begin{descriptionlist}
+        \item[Central gray region (CG)] Controls the freezing behavior.
+        \item[Lateral hypothalamus (LH)] Controls autonomic responses.
+        \item[Paraventricular hypothalamus (PVN)] Controls stress hormones.
+    \end{descriptionlist}
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.9\linewidth]{./img/amygdala_pavlovian.png}
+        \caption{Neural circuits during aversive conditioning}
+    \end{figure}
+\end{remark}
+
+
+
+\section{Memory}
+\marginnote{Memory}
+
+Memory is vulnerable to alteration.
+Once reactivated, the subsequent reconsolidation phase might store a modified version of the memory.
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.6\linewidth]{./img/memory.png}
+    \caption{Memory flow}
+\end{figure}
+
+\begin{remark}
+    This mechanism is useful against traumatic memories.
+\end{remark}
+
+\begin{remark}
+    The amygdala is responsible for storing conditioned responses while the hippocampus recognizes conditioned stimuli.
+
+    Patients with a damaged amygdala only recognize \ac{cs} but do not act with any \ac{cr}.
+    On the other hand, a damaged hippocampus results in patients that present a \ac{cr} without recognizing the \ac{cs}.
+\end{remark}
+
+\begin{casestudy}[Reconsolidation disruption]
+    Propranolol is a drug that disrupts amygdala-specific memory reconsolidation (i.e. the physiological response).
+    A possible therapy to suppress a phobia is to trigger the fear memory and then administer propranolol to prevent its reconsolidation.
+\end{casestudy}
+
+
+
+\section{Learning preconditions}
+
+\subsection{Contiguity}
+\marginnote{Contiguity}
+
+Closeness between the \acl{cs} and the \acl{us}.
+
+\begin{remark}
+    The closer in time the stimuli are presented, the more likely the association will be created.
+\end{remark}
+
+Depending on when the \ac{cs} and \ac{us} are presented, conditioning can be:
+\begin{descriptionlist}
+    \item[Delay conditioning] \marginnote{Delay conditioning}
+        The \ac{cs} is extended through the interstimulus interval (ISI) (i.e. time between the start of the \ac{cs} and the \ac{us}).
+
+    \item[Trace conditioning] \marginnote{Trace conditioning}
+        There is a delay (trace interval) between the \ac{cs} end and the \ac{us} start.
+
+        Learning requires more trials and might be impossible if the trace interval is too long as the mental representation of the \ac{cs} decays.
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.45\linewidth]{./img/contiguity.png}
+    \end{figure}
+\end{descriptionlist}
+
+\begin{casestudy}
+    Two groups of rats were exposed to a 6 seconds tone (\ac{cs}) followed by food delivery (\ac{us}) with a delay of: 
+    \begin{itemize}
+        \item 6 seconds (red).
+        \item 18 seconds (purple).
+    \end{itemize}
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.55\linewidth]{./img/contiguity_rats.png}
+        \caption{Number of entries (i.e. the rat checks the food tray) per second}
+    \end{figure}
+\end{casestudy}
+
+
+\subsection{Contingency}
+\marginnote{Contingency}
+
+Causal relationship between the \acl{cs} and the \acl{us}.
+
+\begin{remark}
+    Learning happens when:
+    \[ \prob{\text{\ac{us} with \ac{cs}}} > \prob{\text{\ac{us} with no \ac{cs}}} \]
+    In other words, the \ac{cs} should provide information regarding the \ac{us}.
+\end{remark}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.6\linewidth]{./img/contingency.png}
+    \caption{Example of contingent and random group}
+\end{figure}
+
+\begin{casestudy}
+    Two groups of rats are exposed to a shock paired with a bell ring.
+    Contiguity is the same but contingency differs.
+
+    Only the group where the shock with the bell is more likely learned the association.
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.8\linewidth]{./img/contingency_rats.png}
+        \caption{Representation of the experiment}
+    \end{figure}
+\end{casestudy}
+
+
+\subsection{Surprise}
+
+\begin{description}
+    \item[Prediction error] \marginnote{Prediction error}
+        Quantitative discrepancy between the expected and experienced outcome.
+\end{description}
+
+\begin{remark}
+    Learning happens when the outcome is different from what was expected.
+\end{remark}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.4\linewidth]{./img/surprise.png}
+    \caption{Learning outcome due to surprise}
+\end{figure}
+
+\begin{casestudy}[Blocking effect]
+    \phantom{} \label{ex:blocking} \\
+    \begin{minipage}{0.65\linewidth}
+        \begin{enumerate}
+            \item A rat is taught that a hissing sound (\ac{cs}) is paired with a sexually receptive mate (\ac{us}).
+            \item A light is added together with the hissing sound.
+            \item When only the light is presented, the rat does not provide a response.
+        \end{enumerate}
+
+        The light is not learned as a \ac{cs} as it does not provide any new information on the \ac{us}.
+    \end{minipage}
+    \begin{minipage}{0.35\linewidth}
+        \begin{figure}[H]
+            \centering
+            \includegraphics[width=\linewidth]{./img/surprise_rats.png}
+        \end{figure}
+    \end{minipage}
+\end{casestudy}
+
+
+
+\section{Computational model}
+
+
+\subsection{Rescorla-Wagner model}
+\marginnote{Rescorla-Wagner model}
+
+Error-driven learning model where the change expectancy is proportional to the difference between predicted and actual outcome:
+\[ \delta_{tr} = R_{tr} - V_{tr} \]
+where:
+\begin{itemize}
+    \item $\delta_{tr}$ is the prediction error.
+    \item $R_{tr} = \begin{cases}
+            1 & \text{if the \ac{us} is delivered at trial $tr$} \\
+            0 & \text{if the \ac{us} is omitted at trial $tr$}
+        \end{cases}$.
+    \item $V_{tr}$ is the association strength (i.e. expectancy of the \ac{us} or the expected value resulting from a given \ac{cs}) at trial $tr$.
+\end{itemize}
+
+Then, the expected value $V_{tr+1}$ is obtained as:
+\[ V_{tr+1} = V_{tr} + \alpha \delta_{tr} \]
+where $\alpha \in [0, 1]$ is the learning rate.
+
+\begin{remark}
+    A lower $\alpha$ is more suited for volatile environments.
+\end{remark}
+
+\begin{remark}
+    The prediction error $\delta$ is:
+    \begin{itemize}
+        \item Positive during acquisition.
+        \item Negative during extinction.
+    \end{itemize}
+    Moreover, the error is larger at the start of acquisition/extinction.
+\end{remark}
+
+\begin{remark}
+    The Rescorla-Wagner model is able to capture the blocking effect (see \hyperref[ex:blocking]{Blocking example}) as
+    the animal computes a single prediction error obtained as the combination of multiple stimuli.
+\end{remark}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.4\linewidth]{./img/rescorla_wagner_curve.png}
+    \caption{Acquisition and extinction in Pavlovian learning according to the Rescorla-Wagner model}
+\end{figure}
+
+\begin{remark}
+    The Rescorla-Wagner model is a trial-level model that only considers the change from trial to trial
+    without considering what happens within and between trials.
+\end{remark}
+
+
+\subsection{Temporal difference model}
+\marginnote{Temporal difference model}
+
+Real-time model based on time steps within a trial instead of monolithic trials.
+At each time $t$ of a trial during which a \ac{cs} is presented,
+the model computes a prediction of the total future reward that will be gained from time $t$ to the end of the trial.
+
+The prediction error is computed as follows:
+\begin{gather*}
+    \delta_t = R_t + V_t - V_{t-1} \\
+    V_{t+1} = V_t + \alpha \delta_t
+\end{gather*}
+
+\begin{itemize}
+    \item At the beginning of learning, the \ac{cs} is presented at time $t_\text{\ac{cs}}$
+        and $V_t = 0$ until the \ac{us} is delivered at time $t_\text{\ac{us}} > t_\text{\ac{cs}}$.
+    \item On the next trial, $V_{t_\text{\ac{us}}} - V_{t_\text{\ac{us}} - 1}$ now generates a positive prediction error that updates $V_{t_\text{\ac{us}} - 1}$.
+    \item On subsequent trials, $V_t$ is updated for each $t$ in between $t_\text{\ac{us}}$ back to $t_\text{\ac{cs}}$.
+\end{itemize}
+
+In other words, the value signal produced by the reward (\ac{us}) is transferred back to an event (\ac{cs}) that predicts the reward.
+
+\begin{casestudy}[Second-order conditioning]
+    Pairing a new \ac{cs} to an existing \ac{cs}.
+
+    \begin{center}
+        \includegraphics[width=0.95\linewidth]{./img/second_order_conditioning.png}
+    \end{center}
+
+    \indenttbox
+    \begin{remark}
+        The Rescorla-Wagner model is not capable of modeling second-order conditioning while 
+        the temporal difference model is.
+    \end{remark}
+\end{casestudy}
+
+
+
+\section{Reward prediction error hypothesis of dopamine}
+
+There is strong evidence that the dopaminergic system is the major neural mechanism of reward and reinforcement.
+
+\begin{description}
+    \item[Response to unexpected rewards] \marginnote{Dopamine response to unexpected rewards}
+        Dopaminergic neurons exhibit a strong phasic response in the presence of an unexpected reward.
+
+        \begin{casestudy}[Monkey that touches food]
+            Some food is put in a box with a hole to reach its content.
+            In the absence of any other stimuli predicting the reward, 
+            a monkey presents a high dopaminergic response when it touches the food.
+            \begin{center}
+                \includegraphics[width=0.55\linewidth]{./img/dopamine_monkey1.png}    
+            \end{center}
+        \end{casestudy}
+
+    \item[Reward discrimination] \marginnote{Dopamine reward discrimination}
+        Dopamine neurons respond differently depending on the actual presence of a reward.
+
+        \begin{casestudy}[Monkey that touches food]
+            The dopaminergic response of a monkey that touches an apple attached to a wire in a box is different 
+            from the response of touching the wire only.
+            \begin{center}
+                \includegraphics[width=0.5\linewidth]{./img/dopamine_monkey2.png}    
+            \end{center}
+        \end{casestudy}
+
+    \item[Magnitude discrimination] \marginnote{Dopamine magnitude discrimination}
+        Dopamine neurons respond differently depending on the amount of reward received.
+
+        \begin{casestudy}[Monkey that drinks]
+            By giving a monkey different amounts of fruit juice in a pseudorandom order,
+            its dopaminergic response is stronger for the highest volume and weaker for the lowest volume.
+            \begin{center}
+                \includegraphics[width=0.7\linewidth]{./img/dopamine_monkey3.png}    
+            \end{center}
+        \end{casestudy}
+
+        \begin{casestudy}[Monkey with juice and images]
+            Using different \acp{cs}, it can be seen that the dopaminergic response differs based on the amount of reward.
+            \begin{center}
+                \includegraphics[width=0.55\linewidth]{./img/dopamine_expected.png}
+            \end{center}
+        \end{casestudy}
+
+        \begin{casestudy}[Monkey with juice and images]
+            After learning the association between a \ac{cs} and \ac{us} (middle graph), a change in the amount of the reward changes the dopaminergic response.
+            \begin{center}
+                \includegraphics[width=0.6\linewidth]{./img/dopamine_expected2.png}
+            \end{center}
+
+            This behavior also involves the context (i.e. the \ac{cs} image that is shown).
+            \begin{center}
+                \includegraphics[width=0.6\linewidth]{./img/dopamine_expected3.png}
+            \end{center}
+        \end{casestudy}
+\end{description}
+
+\begin{remark}
+    With the previous observations, it can be concluded that:
+    \begin{itemize}
+        \item Dopamine neurons increase their firing rate when the reward is unexpectedly delivered or better than expected.
+        \item Dopamine neurons decrease their firing rate when the reward is unexpectedly omitted or worse than expected.
+    \end{itemize}
+\end{remark}
+
+\begin{description}
+    \item[Transfer to \ac{cs}] \marginnote{Dopamine transfer to \ac{cs}}
+        \begin{itemize}
+            \item Before training, an unexpected reward (\ac{us}) causes the dopamine neurons to increase firing (positive prediction error).
+            \item After training, dopamine neurons firing is increased after the \ac{cs} but not following the reward (no prediction error).
+            \item After training, dopamine neurons firing is increased after the \ac{cs} but is decreased if the reward is omitted (negative prediction error).
+        \end{itemize}
+        \begin{casestudy}
+            \phantom{}
+            \begin{center}
+                \includegraphics[width=0.38\linewidth]{./img/dopamine_transfer_cs.png}
+            \end{center}
+        \end{casestudy}
+
+    \item[Response to blocking] \marginnote{Dopamine response to blocking}
+        Dopaminergic response is in line with the blocking effect.
+
+        \begin{casestudy}[Monkey with food and images]
+            \phantom{}\\
+            \begin{minipage}{0.7\linewidth}
+                A monkey is taught to associate images with food.
+                A new \ac{cs} alongside an existing \ac{cs} will not be learned.
+            \end{minipage}
+            \begin{minipage}{0.28\linewidth}
+                \centering
+                \includegraphics[width=0.8\linewidth]{./img/dopamine_blocking.png}
+            \end{minipage}
+        \end{casestudy}
+
+    \item[Probability encoding] \marginnote{Dopamine probability encoding}
+        The phasic activation of dopamine neurons varies monotonically with the reward probability.
+        \begin{casestudy}
+            \phantom{}
+            \begin{center}
+                \includegraphics[width=0.65\linewidth]{./img/dopamine_probability.png}
+            \end{center}
+        \end{casestudy}
+
+    \item[Timing encoding] \marginnote{Dopamine timing encoding}
+        Dopamine response to unexpectedness also involves timing.
+        A dopaminergic response occurs when a reward is given earlier or later than expected.
+
+        \begin{casestudy}
+            After learning that a reward occurs 1 second after the end of the \ac{cs}, 
+            dopamine neurons fire if the timing changes.
+            \begin{center}
+                \includegraphics[width=0.5\linewidth]{./img/dopamine_timing.png}
+            \end{center}
+        \end{casestudy}
+\end{description}
+
+\begin{remark}
+    Dopamine is therefore a signal for the predicted error and not strictly for the reward.
+\end{remark}

src/year1/cognition-and-neuroscience/module1/sections/_rl.tex

@@ -0,0 +1,199 @@
+\chapter{Reinforcement learning}
+
+
+\section{Definitions}
+
+\Acl{rl} (\acs{rl}) methods aim to maximize future reward by mapping the possible states of an environment into actions.
+
+\begin{description}
+    \item[Optimal decision making] \marginnote{Optimal decision making}
+        Aims to maximize rewards and minimize punishments.
+
+        \begin{remark}
+            This is a difficult task as the outcome might be delayed or depend on a series of actions.
+            
+            \begin{descriptionlist}
+                \item[Credit assignment problem]
+                    Determine how the various factors involved in making a decision contributed to the success or failure of it.
+            \end{descriptionlist}
+        \end{remark}
+\end{description}
+
+\begin{remark}
+    Multiple competing sub-systems contribute to learning and controlling behavior in animals.
+
+    \indenttbox
+    \begin{example}[Freud's theory of the mind structure]
+        The mind is composed of three structures:
+        \begin{descriptionlist}
+            \item[Ego]
+                Mainly works at the conscious level.
+                Rational part of the mind that mediates \textit{id} impulses and \textit{superego} inhibitions.
+
+            \item[Superego] 
+                Mainly works at the preconscious level.
+                Includes one's ideals and morals. Strives for perfection.
+
+            \item[Id] 
+                Mainly works at the unconscious level.
+                Irrational part of the mind based on basic impulses that seek immediate gratification.
+        \end{descriptionlist}
+    \end{example}
+\end{remark}
+
+
+\subsection{Learning}
+
+\begin{description}
+    \item[Learning] \marginnote{Learning}
+        Lasting change in response or behavior originated from experience.
+
+    \item[Non-associative learning] \marginnote{Non-associative learning}
+        Change in response or behavior caused by learning the properties of a single stimulus.
+        It can result in:
+        \begin{descriptionlist}
+            \item[Habituation] 
+                A decrease in response to a stimulus that is presented repeatedly.
+                \begin{example}
+                    The first explosion of a firework causes a strong response but the responses to the following ones are much weaker.
+                \end{example}
+
+            \item[Sensitization] 
+                An increase in response to a stimulus that is presented repeatedly.
+                \begin{example}
+                    When the skin itches, one will start scratching it.
+                \end{example}
+        \end{descriptionlist}
+
+    \item[Associative learning] \marginnote{Associative learning}
+        Change in response or behavior caused by learning an association of two or more stimuli/events.
+
+        \begin{descriptionlist}
+            \item[\Acl{rl}] \marginnote{\Acl{rl}}
+                Learn an association between a neutral stimulus (something the body considers irrelevant) and 
+                a reinforcer (something the body considers relevant).
+
+                \begin{description}
+                    \item[Primary reinforcer] \marginnote{Primary reinforcer}
+                        Positive or negative stimulus that is biologically relevant and elicits a response.
+                        \begin{example}
+                            Food, pain, social interactions, \dots
+                        \end{example}
+
+                    \item[Secondary reinforcer] \marginnote{Secondary reinforcer}
+                        Positive or negative stimulus that became relevant following associative learning.
+                        It elicits a response which usually enables a primary reinforcer.
+                \end{description}
+        \end{descriptionlist}
+\end{description}
+
+
+\subsection{Learning systems}
+
+\begin{description}
+    \item[Pavlovian/classical system] \marginnote{Pavlovian system}
+        Form of prediction learning.
+        Learns to predict biologically relevant stimuli to trigger an appropriate response (stimulus-outcome associations).
+
+    \item[Instrumental system] \marginnote{Instrumental system}
+        Form of control learning to learn action-outcome associations.
+        It includes:
+        \begin{descriptionlist}
+            \item[Habitual system] \marginnote{Habitual system}
+                Learn to repeat previously successful actions.
+            \item[Goal-directed system] \marginnote{Goal-directed system}
+                Evaluate actions based on the prior knowledge of their consequences.
+        \end{descriptionlist}
+\end{description}
+
+\begin{remark}
+    Pavlovian and instrumental systems are not independent.
+    By predicting which situations are positive, one can act to reach them through its actions.
+
+    \begin{figure}[H]
+        \centering
+        \includegraphics[width=0.35\linewidth]{./img/learning_systems.png}
+        \caption{Learning systems relationship}
+    \end{figure}
+\end{remark}
+
+
+
+\section{Learning at the neuronal level}
+
+\begin{description}
+    \item[Hebbian plasticity] \marginnote{Hebbian plasticity}
+        Learning and experience change the connections of a neural system.
+
+    \item[Short-term change] \marginnote{Short-term neuronal change}
+        Functional physiological change that modifies the effectiveness of existing synaptic connections (i.e. amount of neurotransmitters).
+        Lasts from seconds up to hours.
+
+    \item[Long-term change] \marginnote{Long-term neuronal change}
+        Structural change that leads to anatomical alterations such as pruning or growth of synapses.
+        Lasts days and can cause further short-term changes.
+\end{description}
+
+\begin{remark}
+    Neuronal changes follow a "use it or lose it" policy:
+    only useful changes will last.
+\end{remark}
+
+\begin{casestudy}[Phantom limb pain]
+    In amputees, the area of the brain responsible for the missing part of the body is overrun by the neighboring sections.
+    In the case of an arm, the area responsible for the face might "conquer" what once was the area of the arm.
+\end{casestudy}
+
+
+
+\section{Dopamine}
+
+\begin{description}
+    \item[Synaptic plasticity]
+        Change the synaptic efficacy by changing the amount of:
+        \begin{descriptionlist}
+            \item[Neurotransmitters] Directly provoke excitatory or inhibitory effects at postsynaptic neurons.
+            \item[Neuromodulators] Neurotransmitters with additional effects.
+        \end{descriptionlist}
+\end{description}
+
+
+\begin{description}
+    \item[Dopamine] \marginnote{Dopamine}
+        Neuromodulator responsible for processes such as motivation, learning, decision-making, addiction, Parkinson's disease, Huntington's disease, \dots.
+
+    \item[Dopaminergic pathways] \marginnote{Dopaminergic pathways}
+        \begin{description}
+            \item[Nigrostriatal pathway] 
+                Originates in the substantia nigra pars compacta (SNc)
+                and primarily projects to the caudate-putemen.
+                
+                \begin{minipage}{0.6\linewidth}
+                    \begin{description}
+                        \item[Basal ganglia motor loop]
+                            Collection of subcortical nuclei responsible for motor control and reinforcement learning.
+                            
+                            The direct pathway initiates movement while the indirect pathway inhibits it.
+
+                            The SNc projects into the striatum and is responsible for releasing dopamine that activates the direct pathway.
+                            The striatum can be seen as the component that uses the reward to influence an action.
+                    \end{description}
+                \end{minipage}
+                \begin{minipage}{0.35\linewidth}
+                    \centering
+                    \includegraphics[width=\linewidth]{./img/basal_ganglia_motor.png}
+                \end{minipage}
+
+            \item[Meso-limbic pathway] 
+                Originates in the VTA and projects to the nucleus accumbens, septum, amygdala and hippocampus.
+
+            \item[Meso-cortical pathway] 
+                Originates in the VTA and projects to the medial prefrontal, cingulate, orbitofrontal and perirhinal cortex.
+        \end{description}
+
+        \begin{figure}[H]
+            \centering
+            \includegraphics[width=0.3\linewidth]{./img/dopaminergic_pathways.png}
+            \caption{Dopaminergic pathways}
+        \end{figure}
+\end{description}

src/year1/cognition-and-neuroscience/module2/ainotes.cls

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