Network inference from non-stationary spike trains
© Tyrcha et al; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
Current approaches to the problem of inferring network connectivity from spike data [1, 2] make a stationarity assumption, which is often not valid. Here we describe a method for inferring both the connectivity of a network in the presence of nonstationarity state and the time-dependent external drive that causes it.
for the model parameters -- the couplings J ij and external inputs h i (t). For weak coupling and/or densely connected networks, we have developed faster alternative algorithms . These are based on expanding the learning rules around mean-field and TAP  equations for m i (t) = ‹S i (t,r)› r . (TAP equations are a generalization of the usual mean-field equations for highly connected random networks.)
We thank Michael Berry for providing the salamander retinal data.
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