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\title{Fundamentals of Computational Neuroscience 2e}
%\author{Thomas Trappenberg}

\begin{document}

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\titlepage

%\date{\today}

\begin{center}  {Chapter 1: Introduction} \end{center}


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\frametitle{\large What is Computational Neuroscience?}

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\begin{quotation}
  \emph{\bf Computational Neuroscience is the theoretical study of the brain
  to uncover the principles and mechanisms that guide the development,
  organization, information processing and mental abilities of the
  nervous system.}
\end{quotation}

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\frametitle{\large Computational/theoretical tools in context} 
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\resizebox{10cm}{!}{\includegraphics{figures/Chapter1/deco2.pdf}}
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\frametitle{\large Levels of organizations in the nervous system}

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\frametitle{\large What is a model?}

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\resizebox{3cm}{!}{\includegraphics{figures/Chapter1/model1.pdf}} \hspace*{1cm}
\resizebox{3cm}{!}{\includegraphics{figures/Chapter1/model2.pdf}} 
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\begin{quotation}
\emph{\bf Models are abstractions of real world systems or
implementations of hypothesis to investigate particular questions about,
or to demonstrate particular features of, a system or hypothesis. }
\end{quotation}


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\frametitle{\large Is there a brain theory?}



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\frametitle{\large Marr's approach}


\begin{enumerate}
\item {\bf Computational theory:} \emph{What is the goal of the computation, why is it appropriate, and what is the logic of the strategy by which it can be carried out?}
\item {\bf Representation and algorithm:} \emph{How can this computational theory be implemented? In particular, what is the representation for the input and output, and what is the algorithm for the transformation?}
\item {\bf Hardware implementation:} \emph{How can the representation and algorithm be realized physically?}
\end{enumerate}

Marr puts great importance to the first level:
\begin{quotation}
"To phrase the matter in another way, an algorithm is likely to be understood more readily by understanding the nature of the problem being solved than by examining the mechanism (and hardware) in which it is embodied."
\end{quotation}

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\frametitle{\large A computational theory of the brain: The anticipating brain}

\begin{quotation}
  \emph{\bf The brain is an anticipating memory system. It learns to represent the world, or more specifically, expectations of the world, which can be used to generate goal directed behavior.}
\end{quotation}

\begin{center}
\resizebox{10cm}{!}{\includegraphics{figures/Chapter1/anticipatingBrain1.pdf}} 
\end{center}


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\frametitle{\large Overview of chapters}

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\resizebox{7cm}{!}{\includegraphics{figures/guide.pdf}} 
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%2. Neurons and conductance-based models

%3. Simpli�ed neuron and population models 

%4. Associators and synaptic plasticity

%5. Cortical organizations and simple networks

%6. Feed-forward mapping networks

%7. Cortical maps and competitive population coding

%8. Recurrent associative networks and episodic memory 

%9. Modular networks, motor control, and reinforcement learning

%10. The cognitive brain


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\frametitle{\large Further Readings} 

\begin{list}{}{\setlength\itemindent{-.3in}  \setlength\rightmargin{.1in} \setlength\itemsep{0.05in}}
\small
\item Patricia S.~Churchland and Terrence J.~Sejnowski, 1992, \emph {The computational Brain}, MIT Press
\item Peter Dayan and Laurence F.~Abbott 2001, \emph{Theoretical Neuroscience}, MIT Press
\item Jeff Hawkins with Sandra Blakeslee 2004, \emph{On Intelligence}, Henry Holt and Company
\item Norman Doidge 2007, \emph{The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science}, James H. Silberman Books
\item Paul W. Glimcher 2003, \emph{Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics}, Bradford Books
\end{list}


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\frametitle{\large Questions} 

\begin{list}
\small
\item What is a model?
\item What are Marr's three levels of analysis?
\item What is a generative model?
\end{list}


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