High-capacity embedding of synfire chains in a cortical network model

Chris Trengove,Cees Van Leeuwen,Markus Diesmann

doi:10.1007/s10827-012-0413-9

Abstract

Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.

Highlights

Evidence of precisely-timed spiking activity suggests that synf ire chains play an important role in representing information in the brain (Abeles 1982; Riehle et al 1997; Prut et al 1998; Shmiel et al 2005; Kilavik et al 2009; Long et al 2010)
The raster plots of all extracted spike packets (Fig. 2(b) and (f)) verify that all spike packets belong to waves that propagate down the chain from the stimulated pool; none occur spontaneously or out of sequence
We presented a cortical network model that allows for the embedding of large numbers of pools linked to form synfire chains

Summary

Introduction

Evidence of precisely-timed spiking activity suggests that synf ire chains play an important role in representing information in the brain (Abeles 1982; Riehle et al 1997; Prut et al 1998; Shmiel et al 2005; Kilavik et al 2009; Long et al 2010). Pools of neurons sequentially linked by feed-forward connections, are capable of generating temporally extended and precisely timed patterns of spiking activity. Each such pattern consists of a packet of near-simultaneous spikes within a pool, that triggers a similar packet in the subsequent pool, and so on, giving rise to wavelike propagation of spiking activity down the chain (Abeles 1982). It modulates spike packet propagation, and the rate of spikes participating in synfire waves, νW These effects have to be combined with those whereby the spiking rate in turn generates the background input This procedure was sufficient to obtain an accurate estimate of the spiking rate

Methods

Results

Conclusion