Dynamics of Information and Emergent Computation in Generic Neural
T. Natschlaeger and W. Maass
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.
Reference: T. Natschlaeger and W. Maass.
Dynamics of information and emergent computation in generic neural
Neural Networks, 18(10):1301-1308, 2005.