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arXiv · 1701.05056

Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

Elena Bertolotti·Raffaella Burioni·Matteo di Volo·Alessandro Vezzani
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Abstract

We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons $f_I$ and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on $f_I$, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

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Elena Bertolotti, Raffaella Burioni, Matteo di Volo, Alessandro Vezzani. 2017-01-18. Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity. https://doi.org/10.1103/physreve.95.012308

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