\documentclass[addpoints,11pt]{exam} \usepackage{url} \usepackage{color} \usepackage{hyperref} \pagestyle{headandfoot} \runningheadrule \firstpageheadrule \firstpageheader{Scientific Computing}{Project Assignment}{11/02/2015 -- 11/05/2015} %\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014} \firstpagefooter{}{}{{\bf Supervisor:} Jan Grewe} \runningfooter{}{}{} \pointsinmargin \bracketedpoints %\printanswers %\shadedsolutions \begin{document} %%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%% \sffamily % \begin{flushright} % \gradetable[h][questions] % \end{flushright} \begin{center} \input{../disclaimer.tex} \end{center} %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% \section*{Reverse reconstruction of the stimulus evoking neuronal responses.} To analyse encoding properties of a neuron one often calculates the Spike-Triggered-Average (STA). \[ STA(\tau) = \frac{1}{\langle n \rangle} \left\langle \displaystyle\sum_{i=1}^{n}{s(t_i - \tau)} \right\rangle \] The STA is the average stimulus that led to a spike in the neuron and can calculated by cutting out snippets form the stimulus centered on the respective spike time. The Spike-Triggered-Average can be used to reconstruct the stimulus a neuron has been stimulated with. \begin{questions} \question In the accompanying files you find the spike responses of P-units and pyramidal neurons of the weakly electric fish \textit{Apteronotus leptorhynchus}. The respective stimuli are stored in separate files. The data is sampled with 20\,kHz temporal resolution and spike times are given in seconds. Start with the P-unit and, in the end, apply the same functions to the pyramidal data. \begin{parts} \part Estimate the STA and plot it. \part Implement a function that does the reconstruction of the stimulus using the STA. \part Implement a function that estimates the reconstruction error using the mean-square-error and express it relative to the variance of the original stimulus. \begin{equation} err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x})^2, \end{equation} with $N$ the number of data points, $x_i$ the current value and $\bar{x}$, the average of all $x$. \part Analyze the robustness of the reconstruction: Estimate the STA with less and less data and estimate the reconstruction error. \part Plot the reconstruction error as a function of the data amount used to estimate the STA. \part Repeat the above steps for the pyramidal neuron, what do you observe? \end{parts} \end{questions} \end{document}