| Numerous methods have already been developed to estimate the information contained
in single spike trains. In this article we explore efficient methods for estimating the information
contained in the simultaneous firing activity of hundreds of neurons. Obviously such methods
are needed to analyze data from multi-unit recordings. We test these methods on generic neural
microcircuit models consisting of 800 neurons, and analyze the temporal dynamics of information
about preceding spike inputs in such circuits. It turns out that information spreads with high speed
in such generic neural microcircuit models, thereby supporting – without the postulation of any
additional neural or synaptic mechanisms – the possibility of ultra-rapid computations on the first
input spikes. |