Abstract
Reliable propagation of slow-modulations of the firing rate across multiple layers of a feedforward network (FFN) has proven difficult to capture in spiking neural models. In this paper, we explore necessary conditions for reliable and stable propagation of time-varying asynchronous spikes whose instantaneous rate of changes—in fairly short time windows [20–100] msec—represents information of slow fluctuations of the stimulus. Specifically, we study the effect of network size, level of background synaptic noise, and the variability of synaptic delays in an FFN with all-to-all connectivity. We show that network size and the level of background synaptic noise, together with the strength of synapses, are substantial factors enabling the propagation of asynchronous spikes in deep layers of an FFN. In contrast, the variability of synaptic delays has a minor effect on signal propagation.
Highlights
Information in the brain is encoded by either the number of spikes in a relatively long time window, i.e., rate code, or by their precise timing, i.e., temporal code (Abeles et al, 1994; Panzeri et al, 2001, 2017; Montemurro et al, 2007; Kremkow et al, 2010; London et al, 2010; Zuo et al, 2015; Runyan et al, 2017; Noble, 2019)
We show that unlike the variability of synaptic delays that has a minor effect on signal propagation, network size and the level of background synaptic noise are substantial factors enabling the propagation of asynchronous spikes in deep layers of an feedforward network (FFN)
To explore the effects of network size and the level of background synaptic noise in the propagation of slowly timevarying asynchronous spikes, we vary these parameters in an FFN consisting of two layers and calculate the coding fraction (CF)
Summary
Information in the brain is encoded by either the number of spikes in a relatively long time window, i.e., rate code, or by their precise timing, i.e., temporal code (Abeles et al, 1994; Panzeri et al, 2001, 2017; Montemurro et al, 2007; Kremkow et al, 2010; London et al, 2010; Zuo et al, 2015; Runyan et al, 2017; Noble, 2019). Information is carried by groups of neurons that fire synchronously, as in synfire chains (Abeles et al, 1994; Diesmann et al, 1999), whereas in rate coding, neuronal firing ideally remains asynchronous across neurons (Litvak et al, 2003). Information processing in a hierarchically organized cortical system relies on the reliable propagation of synchronous and asynchronous spikes (Joglekar et al, 2018). The reliable propagation of synchronous spikes (temporal code) is well-understood and relatively easy to implement in computer models (Kumar et al, 2008, 2010; Joglekar et al, 2018). The reliable propagation of rate-modulated asynchronous spiking (rate code) is poorly understood and remains challenging to implement in computer models (Litvak et al, 2003). In all of the scenarios, rate-based coding is compromised
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