2 research outputs found
On the information in spike timing: neural codes derived from polychronous groups
There is growing evidence regarding the importance of spike timing in neural
information processing, with even a small number of spikes carrying
information, but computational models lag significantly behind those for rate
coding. Experimental evidence on neuronal behavior is consistent with the
dynamical and state dependent behavior provided by recurrent connections. This
motivates the minimalistic abstraction investigated in this paper, aimed at
providing insight into information encoding in spike timing via recurrent
connections. We employ information-theoretic techniques for a simple reservoir
model which encodes input spatiotemporal patterns into a sparse neural code,
translating the polychronous groups introduced by Izhikevich into codewords on
which we can perform standard vector operations. We show that the distance
properties of the code are similar to those for (optimal) random codes. In
particular, the code meets benchmarks associated with both linear
classification and capacity, with the latter scaling exponentially with
reservoir size