Effects of rate and cross-correlation in recurrent networks of coincidence detectors

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“Effects of rate and cross-correlation in recurrent networks of coincidence detectors” by S. Mikula and E. Niebur. Society for Neuroscience Abstracts, 2005.

Abstract

Key words: coincidence detecotr, computation, synchrony, neural coding, Abstract: Considerable experimental evidence accumulated over past decades has underscored the importance of neuronal firing rates and inter-neuronal cross-correlations within distributed neuronal populations for processing information and neural coding. However, the general principles underlying the respective roles of firing rate and synchrony are unclear. In this work, we address the issue of neuronal coding through combinatorial analyses of a relatively simple theoretical model: an asymmetric and recurrent network of coincidence detectors (also known as McCulloch-Pitts neurons). We derive a general method for the determination of exact analytical solutions for firing rates and pairwise correlations within a network of coincidence detectors that involves summations over probabilities of suprathreshold events, and further describe a series of exact analytical results for this model that offers new insight into how rate and synchrony factor into neural coding. Exact solutions are obtained for the coincidence detector with autaptic connections, at the individual level, and recurrent asymmetric connections, at the network level. All of the derived solutions are expressed in terms of neuronal firing rates and cross-correlations, thereby permitting experimental verification and prediction. Our results support the hypothesis that (i) rate and synchrony are tightly coupled within neuronal populations, and (ii) it is unlikely for neuronal populations to employ one type of code independently of the other. Understanding how neurons and networks employ firing rates, cross-correlations, and higher-order interactions will be critical for deciphering the neural code and for elucidating the general principles of neural computation. Supported by NIH-NINDS grant NS43188-01A1

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BibTeX entry:

@article{Mikula_Niebur05b,
   author = {S. Mikula and E. Niebur},
   title = {Effects of rate and cross-correlation in recurrent networks of
	coincidence detectors},
   journal = {Society for Neuroscience Abstracts},
   pages = {283.12},
   address = {Washington, DC},
   year = {2005}
}