Ising Models for Inferring Network Structure From Spike Data

Details Ising Models for Inferring Network Structure From Spike Data Ising Models for Inferring Network Structure From Spike Data

2011-06-09

arxiv.org/abs/1106.1752v1

Biology

Quantitative Biology

Quantitative Methods

To appear in "Principles of Neural Coding", edited by Stefano Panzeri and Rodrigo Quian Quiroga

Scientific paper

Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.

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