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

Bertolotti, Elena; Burioni, Raffaella; Volo, Matteo di; Vezzani, A.

doi:10.1103/physreve.95.012308

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…Based on this, to study the synaptic dynamics, we considered the phenomenological model proposed in [22]. In this model, each directional synaptic connection from a presynaptic neuron [44] is represented by the set of ODEs…”

Section: Methodsmentioning

confidence: 99%

Analytical solutions for the short-term plasticity

Protachevicz,

Batista,

Caldas

et al. 2024

Chaos, Solitons & Fractals

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Section: Methodsmentioning

confidence: 99%

Analytical solutions for the short-term plasticity

Protachevicz,

Batista,

Caldas

et al. 2024

Chaos, Solitons & Fractals

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“…To further investigate the global dynamical behavior of the neural networks, we further introduce the order parameter [27,28], R, together with its the variance in time, metastability [18,20,29], M et, given by:…”

Section: Models and Methodsmentioning

confidence: 99%

Noise-induce coexisting firing patterns in hybrid-synaptic interacting networks

Wang¹,

Zhang²,

Zheng³

et al. 2022

Preprint

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Synaptic noise plays a major role in setting up coexistence of various firing patterns, but the precise mechanisms whereby these synaptic noise contributes to coexisting firing activities are subtle and remain elusive. To investigate these mechanisms, neurons with hybrid synaptic interaction in a balanced neuronal networks have been recently put forward. Here we show that both synaptic noise intensity and excitatory weights can make a greater contribution than variance of synaptic noise to the coexistence of firing states with slight modification parameters. The resulting statistical analysis of both voltage trajectories and their spike trains reveals two forms of coexisting firing patterns: time-varying and parameter-varying multistability. The emergence of time-varying multistability as a format of metstable state has been observed under suitable parameters settings of noise intensity and excitatory synaptic weight. While the parameter-varying multistability is accompanied by coexistence of synchrony state and metastable (or asynchronous firing state) with slightly varying noise intensity and excitatory weights. Our results offer a series of precise statistical explanation of the intricate effect of synaptic noise in neural multistability. This reconciles previous theoretical and numerical works, and confirms the suitability of various statistical methods to investigate multistability in a hybrid synaptic interacting neuronal networks.

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“…Eq. ( 7) can be modified and extended to take into account different topologies, including the inhomogeneities of the system [33] and inhibition [34]. Eq.s (3-7) define our network model which we will study numerically by an event-driven approach [35,36] (see Appendix for details).…”

Section: The C-lif Modelmentioning

confidence: 99%

Order symmetry breaking and broad distribution of events in spiking neural networks with continuous membrane potential

Stucchi,

Pittorino,

di Volo

et al. 2021

Preprint

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We introduce an exactly integrable version of the well-known leaky integrate-and-fire (LIF) model, with continuous membrane potential at the spiking event, the c-LIF. We investigate the dynamical regimes of a fully connected network of excitatory c-LIF neurons in the presence of short-term synaptic plasticity. By varying the coupling strength among neurons, we show that a complex chaotic dynamics arises, characterized by scale free avalanches. The origin of this phenomenon in the c-LIF can be related to the order symmetry breaking in neurons spike-times, which corresponds to the onset of a broad activity distribution. Our analysis uncovers a general mechanism through which networks of simple neurons can be attracted to a complex basin in the phase space.

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

Cited by 16 publications

References 37 publications

Analytical solutions for the short-term plasticity

Analytical solutions for the short-term plasticity

Noise-induce coexisting firing patterns in hybrid-synaptic interacting networks

Order symmetry breaking and broad distribution of events in spiking neural networks with continuous membrane potential

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