Abstract
This numerical study documents and analyzes emergent spiking behavior in local neuronal populations. Emphasis is given to a phenomenon we call clustering, by which we refer to a tendency of random groups of neurons large and small to spontaneously coordinate their spiking activity in some fashion. Using a sparsely connected network of integrate-and-fire neurons, we demonstrate that spike clustering occurs ubiquitously in both high firing and low firing regimes. As a practical tool for quantifying such spike patterns, we propose a simple scheme with two parameters, one setting the temporal scale and the other the amount of deviation from the mean to be regarded as significant. Viewing population activity as a sequence of events, meaning relatively brief durations of elevated spiking, separated by inter-event times, we observe that background activity tends to give rise to extremely broad distributions of event sizes and inter-event times, while driving a system imposes a certain regularity on its inter-event times, producing a rhythm consistent with broad-band gamma oscillations. We note also that event sizes and inter-event times decorrelate very quickly. Dynamical analyses supported by numerical evidence are offered.










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Notes
1 If the refractory period is absent or too short, g E may increase with each population spike. Our 4ms refractory period was chosen to quench this build-up in conductance and subsequent runaway spiking. It has virtually no effect on regimes other than those with very small α.
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Action Editor: David Terman
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The authors declare that they have no conflict of interest
This research is supported in part by NSF Grant DMS-1101594.
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Chariker, L., Young, LS. Emergent spike patterns in neuronal populations. J Comput Neurosci 38, 203–220 (2015). https://doi.org/10.1007/s10827-014-0534-4
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DOI: https://doi.org/10.1007/s10827-014-0534-4