Dynamics of information and emergent computation in generic neural microcircuit models
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 supportingwithout the postulation of any additional neural or synaptic mechanismsthe possibility of ultra-rapid computations on the first input spikes.
neural microcircuit spiking neurons information theoretic methods neural coding computational power dynamic synapses linear regression bayesian classifier