🤖 Web developer stuff

.github/prepare_web_viewer.py

@@ -1,68 +0,0 @@
-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

@@ -1,30 +0,0 @@
-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

@@ -1,94 +0,0 @@
-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

@@ -1,38 +0,0 @@
-<!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

@@ -1,23 +0,0 @@
-<!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

@@ -1,65 +0,0 @@
-name: Compile LaTeX
-
-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:
-    name: Compile notes
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Checkout current pdfs branch
-        uses: actions/checkout@v4
-        with:
-          ref: pdfs
-          path: .currpdfs
-
-
-      - name: Prepare output directory
-        run: |
-          mkdir ${GITHUB_WORKSPACE}/.compiled
-          cp ${GITHUB_WORKSPACE}/LICENSE-pdf ${GITHUB_WORKSPACE}/.compiled/LICENSE
-
-      - name: Compile
-        run: |
-          docker pull ghcr.io/notxia/unibo-ai-notes:main
-          docker run -v ${GITHUB_WORKSPACE}:/notes ghcr.io/notxia/unibo-ai-notes:main
-
-      - 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: .compiled
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          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

@@ -1,49 +0,0 @@
-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

@@ -1,42 +0,0 @@
-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

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

Dockerfile

@@ -1,10 +0,0 @@
-FROM archlinux:latest
-
-WORKDIR /notes
-
-RUN pacman --noconfirm -Syu && pacman --noconfirm -S git texlive-basic texlive-latex texlive-binextra texlive-mathscience texlive-latexextra texlive-fontsextra texlive-bibtexextra biber perl perl-mozilla-ca
-RUN ln -s /usr/bin/vendor_perl/biber /usr/bin/biber
-
-RUN git config --global --add safe.directory /notes
-
-CMD ["bash", "./utils/compile.sh", "./src", "./.compiled", "./.currpdfs"]

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 Tian Cheng Xia
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

LICENSE-pdf

@@ -1,427 +0,0 @@
-Attribution-ShareAlike 4.0 International
-
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-does not provide legal services or legal advice. Distribution of
-Creative Commons public licenses does not create a lawyer-client or
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-Creative Commons may be contacted at creativecommons.org.

README.md

@@ -1,8 +0,0 @@
-# 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.

index.html

@@ -0,0 +1,77 @@
+<!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 -->
+<h3>Fundamentals of Artificial Intelligence and Knowledge Representation</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/fundamentals-of-ai-and-kr/module1/faikr1.pdf'>FAIKR module 1</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/fundamentals-of-ai-and-kr/module2/faikr2.pdf'>FAIKR module 2</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/fundamentals-of-ai-and-kr/module3/faikr3.pdf'>FAIKR module 3</a></h4></li>
+</ul>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/machine-learning-and-data-mining/dm-ml.pdf'>Machine Learning and Data Mining</a></h3>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/statistical-and-mathematical-methods-for-ai/smm.pdf'>Statistical and Mathematical Methods for Artificial Intelligence</a></h3>
+<h3>Cognition and Neuroscience</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/cognition-and-neuroscience/module1/cn1.pdf'>CN module 1</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/cognition-and-neuroscience/module2/cn2.pdf'>CN module 2</a></h4></li>
+</ul>
+<h3>Combinatorial Decision Making and Optimization</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/combinatorial-decision-making-and-optimization/module1/cdmo1.pdf'>CDMO module 1</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/combinatorial-decision-making-and-optimization/module2/cdmo2.pdf'>CDMO module 2</a></h4></li>
+</ul>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/deep-learning/dl.pdf'>Deep Learning</a></h3>
+<h3>Image Processing and Computer Vision</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/image-processing-and-computer-vision/module1/ipcv1.pdf'>IPCV module 1</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/image-processing-and-computer-vision/module2/ipcv2.pdf'>IPCV module 2</a></h4></li>
+</ul>
+<h3>Languages and Algorithms for Artificial Intelligence</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/languages-and-algorithms-for-ai/module2/laai2.pdf'>LAAI module 2</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year1/languages-and-algorithms-for-ai/module3/laai3.pdf'>LAAI module 3</a></h4></li>
+</ul>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/artificial-intelligence-in-industry/a3i.pdf'>Artificial Intelligence in Industry</a></h3>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/machine-learning-for-computer-vision/ml4cv.pdf'>Machine Learning for Computer Vision</a></h3>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/natural-language-processing/nlp.pdf'>Natural Language Processing</a></h3>
+<h3><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/distributed-autonomous-systems/das.pdf'>Distributed Autonomous Systems</a></h3>
+<h3>Ethics in Artificial Intelligence</h3>
+<ul>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/ethics-in-ai/module1/ethics1.pdf'>Ethics module 1</a></h4></li>
+<li><h4><a href='https://raw.githubusercontent.com/NotXia/unibo-ai-notes/pdfs/year2/ethics-in-ai/module2/ethics2.pdf'>Ethics module 2</a></h4></li>
+</ul>
+
+<!-- end-toc -->
+</body>
+</html>

src/ainotes.cls

@@ -1,150 +0,0 @@
-\NeedsTeXFormat{LaTeX2e}[]
-\ProvidesClass{ainotes}
-
-\LoadClass{scrreprt}
-
-
-\usepackage{geometry}
-\usepackage{graphicx, xcolor}
-\usepackage{amsmath, amsfonts, amssymb, amsthm, mathtools, bm, upgreek, cancel, bbm, siunitx, thmtools}
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-\usepackage[nameinlink]{cleveref}
-\usepackage[all]{hypcap} % Links hyperref to object top and not caption
-\usepackage[inline]{enumitem}
-\usepackage{marginnote}
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-\usepackage{scrhack, algorithm, listings}
-\usepackage{array, makecell, multirow, booktabs}
-\usepackage{acro}
-\usepackage{subcaption}
-\usepackage{eurosym}
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-\usepackage[most]{tcolorbox}
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-    breakatwhitespace = false, 
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-\lstset{style=mystyle}
-\lstset{language=Python}
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-
-\NewDocumentEnvironment{descriptionlist}{}{%
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-\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
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-
-\newenvironment{example}{%
-    \begin{marginbar}{lightgray}{0}{thick}
-    \begin{privateexample}
-}{%
-    \end{privateexample}
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-\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

@@ -1,15 +0,0 @@
-{
-    "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

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

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

@@ -1,43 +0,0 @@
-\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

@@ -1,605 +0,0 @@
-\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

@@ -1,133 +0,0 @@
-\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

@@ -1,777 +0,0 @@
-\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

@@ -1,496 +0,0 @@
-\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}