Abstract
Biological neural networks are shaped by a large number of plasticity mechanisms operating at different time scales. How these mechanisms work together to sculpt such networks into effective information processing circuits is still poorly understood. Here we study the spontaneous development of synfire chains in a self-organizing recurrent neural network (SORN) model that combines a number of different plasticity mechanisms including spike-timing-dependent plasticity, structural plasticity, as well as homeostatic forms of plasticity. We find that the network develops an abundance of feed-forward motifs giving rise to synfire chains. The chains develop into ring-like structures, which we refer to as “synfire rings.” These rings emerge spontaneously in the SORN network and allow for stable propagation of activity on a fast time scale. A single network can contain multiple non-overlapping rings suppressing each other. On a slower time scale activity switches from one synfire ring to another maintaining firing rate homeostasis. Overall, our results show how the interaction of multiple plasticity mechanisms might give rise to the robust formation of synfire chains in biological neural networks.
Highlights
Precise repetitions of neural activity patterns may serve as an infrastructure for numerous neural functions including sensory processing, motor control, and cognition
We study the spontaneous development of synfire chains in a self-organizing recurrent neural network (SORN) model that combines a number of different plasticity mechanisms including spike-timing-dependent plasticity, structural plasticity, as well as homeostatic forms of plasticity
We show that synfire chains form spontaneously from randomly initialized self-organizing recurrent networks (SORNs) in the absence of any structured external inputs
Summary
Precise repetitions of neural activity patterns may serve as an infrastructure for numerous neural functions including sensory processing, motor control, and cognition. Synfire chains have been proposed as a fundamental network structure of the nervous system, which can guarantee a fixed level of network activity while allowing to learn and reproduce complicated spatio-temporal firing patterns (Abeles, 1982). Successful learning required that the same training stimulus was shown to the system repeatedly These stimuli, represented as sequences of activation patterns, determined the network dynamics which in turn determined the network connectivity due to STDP and other learning rules. Along similar lines, Levy et al (2001) studied networks in the distributed synchrony activity mode whose dynamics depended on an STDP learning rule and external input. Doursat and Bienenstock (2006) proposed an approach in which a set of seed neurons, a variant of spatiotemporal input, was found essential for the growth of synfire chains. Other recent approaches employed both different variants of STDP rules and spatiotemporal patterns of stimulation (Iglesias and Villa, 2008; Fiete et al, 2010; Waddington et al, 2012)
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