  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.
