On the information in spike timing: neural codes derived from polychronous groups
This work addresses the gap in computational models for spike timing coding, which is incremental as it builds on existing concepts like polychronous groups.
The paper tackled the problem of understanding information encoding in spike timing via recurrent connections, showing that a simple reservoir model can encode input spatiotemporal patterns into a sparse neural code with distance properties similar to optimal random codes, achieving exponential scaling of capacity with reservoir size.
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.