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Add CN introduction
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src/cognition-and-neuroscience/ainotes.cls
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src/cognition-and-neuroscience/ainotes.cls
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../ainotes.cls
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src/cognition-and-neuroscience/cn.tex
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src/cognition-and-neuroscience/cn.tex
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\documentclass[11pt]{ainotes}
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\title{Cognition and Neuroscience}
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\date{2023 -- 2024}
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\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}}
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\begin{document}
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\makenotesfront
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\input{./sections/_introduction.tex}
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\end{document}
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src/cognition-and-neuroscience/img/doubt.png
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src/cognition-and-neuroscience/img/doubt.png
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src/cognition-and-neuroscience/sections/_introduction.tex
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src/cognition-and-neuroscience/sections/_introduction.tex
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\chapter{Introduction}
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\section{Definitions}
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\begin{description}
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\item[Neuroscience] \marginnote{Neuroscience}
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Study of the nervous system (structure aspects) on various levels of detail:
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\begin{descriptionlist}
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\item[Molecular] Proteins and molecular signaling of the nervous system.
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\item[Cellular] Morphological and physiological properties of neurons.
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\item[Neural system] Creation and functioning of networks of neurons.
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\end{descriptionlist}
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\item[Cognition] \marginnote{Cognition}
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Mental processes (function aspects) that react to inputs.
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It involves processes regarding the acquisition, storage, manipulation, and retrieval of information.
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\begin{descriptionlist}
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\item[Perception] Information from the environment.
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\item[Attention] Focus on a specific stimulus in the environment.
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\item[Learning] Merging new information with prior knowledge.
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\item[Memory] Encoding, storing, and retrieving information.
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\item[Action] Interact with the environment using perceived information.
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\item[Language] Understanding and producing spoken or written thoughts.
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\item[Higher reasoning] Decision-making and problem-solving.
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\end{descriptionlist}
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\item[Biomimicry] \marginnote{Biomimicry}
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Solving problems by taking inspiration from elements of nature.
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As proof of general intelligence\footnote{\includegraphics[width=1cm]{img/doubt.png}},
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the human brain is taken as the model for artificial intelligence.
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Moreover, a successful brain-inspired AI application can
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provide a possibly plausible explanation of the functioning of the brain.
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However, a brain differs from a computer in many aspects:
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\begin{itemize}
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\item Hardware and software are distinct while mind and brain are not.
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\item Machines learn by exploiting the capability of using a large memory
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while brains have limited capacity but high generalization ability.
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\item Brains produce both electrical and biochemical signals and
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have feedforward, feedback, and recurrent connections
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while machines typically only employ feedforward connections.
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\end{itemize}
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\begin{description}
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\item[Structure emulation]
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Mimic or reverse engineer the structure of the brain (e.g. Blue Brain Project).
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\item[Function emulation]
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Mimic a neural system on the algorithmic level (e.g. Deep Mind).
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\end{description}
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\item[Cognitive neuroscience] \marginnote{Cognitive neuroscience}
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Study of the relationship between the physical brain and the intangible mind (thoughts, ideas).
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In other words, it studies the relationship between structure and function.
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\begin{example}[Severed Corpus Callosum\footnote{\url{https://www.youtube.com/watch?v=lfGwsAdS9Dc}}]
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Normally, the right and left hemispheres of the brain can communicate.
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Moreover, the left visual field is sent to the right hemisphere and
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the right visual field is sent to the left hemisphere.
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In patients where the hemispheres are split, a text shown on the right visual side is recognized as
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the speech capabilities are located in the left hemisphere,
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while a text shown on the left visual side does not trigger any speech reaction.
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\end{example}
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\end{description}
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\section{Neuroscience history}
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Two main schools of thought emerged and are still the subject of ongoing debates:
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\begin{descriptionlist}
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\item[Localizationism] \marginnote{Localizationism}
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Specific regions of the brain are responsible for particular faculties.
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Assuming localizationism, 52 distinct regions with different neurons can be identified.
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\item[Aggregate field theory] \marginnote{Aggregate field theory}
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The brain works as a whole for mental functions.
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\end{descriptionlist}
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\subsection{Neuron doctrine}
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\marginnote{Neuron doctrine}
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The nervous system is made of a discrete amount of individual neurons (and not a continuous tissue).
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\begin{description}
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\item[Principle of dynamic polarization]
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Electrical signals in a neuron flow only in a single direction.
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\item[Principle of connectional specificity]
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Neurons do not connect randomly but make specific connections at particular contact points.
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\item[Synapse] \marginnote{Synapse}
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Point of contact of two neurons. A synapse can be chemical or electrical.
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\end{description}
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\section{Cognitive science history}
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\begin{description}
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\item[Rationalism] \marginnote{Rationalism}
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All knowledge can be derived through reasoning, without sensory experiences.
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\item[Empiricism] \marginnote{Empiricism}
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The brain starts as a blank slate and knowledge is added through sensory experiences.
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\item[Associationism] \marginnote{Associationism}
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Inspired by empiricism.
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Learning happens by correlating individual experiences (e.g. actions followed by a reward will be repeated).
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\item[Behaviorism] \marginnote{Behaviorism}
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Inspired by empiricism.
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Everyone has the same neural basis that is improved through learning.
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Learning only involves observable behaviors.
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\end{description}
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\begin{remark}
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Associationism and behaviorism are not able to explain all types of learning (e.g. language).
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\end{remark}
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\begin{description}
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\item[Cognitivism] \marginnote{Cognitivism}
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The psychological and biological levels of an individual cannot be separated.
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Learning is based on the biology of the neurons.
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\end{description}
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