Computational Neuroscience: Mathematical and Statistical Perspectives

By Robert E. Kass et al

Published on Dec. 8, 2017

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Table of Contents

1. INTRODUCTION
2. Neurons as Electrical Circuits
3. Receptive Fields and Tuning Curves

Summary

This document discusses the importance of mathematical and statistical models in computational neuroscience, focusing on the modeling of neural activity in the form of action potentials and spike trains. It explores the interaction between mechanistic and statistical approaches in understanding neural dynamics. The history of brain science as a computational device and the development of artificial neural networks are also highlighted. Additionally, it delves into the electrical properties of neurons, the Hodgkin-Huxley model, and the concept of receptive fields and tuning curves in neuronal responses to stimuli.