  The aim of this paper is to investigate various information-theoretic
measures, including entropy, mutual information, and some systematic measures
that based on mutual information, for a class of structured spiking neuronal
network. In order to analyze and compute these information-theoretic measures
for large networks, we coarse-grained the data by ignoring the order of spikes
that fall into the same small time bin. The resultant coarse-grained entropy
mainly capture the information contained in the rhythm produced by a local
population of the network. We first proved that these information theoretical
measures are well-defined and computable by proving the stochastic stability
and the law of large numbers. Then we use three neuronal network examples, from
simple to complex, to investigate these information-theoretic measures. Several
analytical and computational results about properties of these
information-theoretic measures are given.
