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- W2963670104 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." @default.
- W2963670104 created "2019-07-30" @default.
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- W2963670104 date "2017-01-01" @default.
- W2963670104 modified "2023-09-23" @default.
- W2963670104 title "Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity" @default.
- W2963670104 hasPublicationYear "2017" @default.
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