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+\documentclass[11pt]{ainotes}
+
+\title{Cognition and Neuroscience}
+\date{2023 -- 2024}
+\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}}
+
+\begin{document}
+    
+    \makenotesfront
+    \input{./sections/_introduction.tex}
+    
+\end{document}
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+\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{example}[Severed Corpus Callosum\footnote{\url{https://www.youtube.com/watch?v=lfGwsAdS9Dc}}]
+            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{example}
+\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}
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