comp_neuro

Overview

Exercises and notes are from textbook "Theoretical Neuroscience" (Dayan and Abbott).

Exercises are written in Python, with a few in MATLAB, and involve statistical analysis and visualization using matplotlib. Notes are primarily in LATEX (with Ch. 1 notes in a Jupyter notebook). Each chapter has notes, and most have solutions to selected exercises.

See call.pdf for a list of all chapter exercises. Data required for the exercises is in the data folder of each chapter directory.

Contents

Ch 1. Neural encoding I: Firing rates and spike statistics

Estimating neuron firing rates and comparing them with mathematical models. Includes a Poisson spike generator, a white-noise simulator, and a few nice graphs.

Ch 2. Neural encoding II: Reverse correlation and visual receptive fields

Estimating neuron firing rates with linear filters and by adding static nonlinearities. Includes a few more nice graphs.

Ch 3. Neural decoding

Estimating the stimulus based on firing rates from certain experiments.

Ch 4. Information theory

Exercises were mainly math-based and are not included here.