Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

Abstract

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.

Shaping Neural Circuits by High Order Synaptic Interactions

“…This nucleus is a compelling candidate for a brain area that can organize autonomously to produce structured dynamics, since auditory deprived songbirds generate a song with a stereotypical temporal course [51,52]. However, in almost all theoretical works that addressed how local plasticity rules give rise to temporal sequences of neural activity, it was necessary to provide some form of structured input into the network in order to robustly produce the sequential neural activity [25,[53][54][55]. Similarly, structured inputs were required in order to robustly produce self connected assemblies, which give rise to another useful form of neural dynamics, characterized by multiple stable states [27][28][29][30][31].…”

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network

“…Other models have been proposed for theta phase precession [87][88][89][90] and replay [91][92][93][94][95][96]. For example, one grid cell model uses after-spike depolarization within a 1D continuous attractor network to generate phase precession and theta sequences [89].…”

“…Several of them encode replay trajectories into synaptic weights, either through hard-wiring or a learning mechanism [94][95][96]. Two models have suggested that replays originate from wavefronts of activity propagating through networks of place cells [91,93]. These wavefronts then enhance connections through the network though spike-timing-dependent plasticity rules, which could account for the ability of reward to modulate hippocampal replay [21,23].…”

Dynamical Ising model of spatially-coupled ecological oscillators

“…The dynamics of flocking in birds or schooling in fish and similar behavior in bacteria [8] essentially are examples of synchrony across a large scale determined by local interactions [9][10][11]. At the suborganism scale, how the dynamics of individual neurons lead to collective behavior is a key question in neuroscience [12]. The synchrony of neural oscillators is thought to play an important role in behavior [6] and in various pathologies [13].…”