Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks

Raoul-Martin Memmesheimer and Marc Timme

PLOS Computational Biology, 2012, vol. 8, issue 4, 1-9

Abstract: Despite the current debate about the computational role of experimentally observed precise spike patterns it is still theoretically unclear under which conditions and how they may emerge in neural circuits. Here, we study spiking neural networks with non-additive dendritic interactions that were recently uncovered in single-neuron experiments. We show that supra-additive dendritic interactions enable the persistent propagation of synchronous activity already in purely random networks without superimposed structures and explain the mechanism underlying it. This study adds a novel perspective on the dynamics of networks with nonlinear interactions in general and presents a new viable mechanism for the occurrence of patterns of precisely timed spikes in recurrent networks. Author Summary: Most nerve cells in neural circuits communicate by sending and receiving short stereotyped electrical pulses called action potentials or spikes. Recent neurophysiological experiments found that under certain conditions the neuronal dendrites (branched projections of the neuron that transmit inputs from other neurons to the cell body (soma)) process input spikes in a nonlinear way: If the inputs arrive within a time window of a few milliseconds, the dendrite can actively generate a dendritic spike that propagates to the neuronal soma and leads to a nonlinearly amplified response. This response is temporally highly precise. Here we consider an analytically tractable model of spiking neural circuits and study the impact of such dendritic nonlinearities on network activity. We find that synchronous spiking activity may robustly propagate through the network, even if it exhibits purely random connectivity without additionally superimposed structures. Such propagation may contribute to the generation of spike patterns that are currently discussed to encode information about internal states and external stimuli in neural circuits.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1002384

DOI: 10.1371/journal.pcbi.1002384

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