Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule
نویسندگان
چکیده
In this paper, we numerically investigate two distinct phenomena, coherence resonance (CR) and self-induced stochastic (SISR), in multiplex neural networks the presence of spike-timing-dependent plasticity (STDP). The high degree CR achieved one layer network turns out to be more robust than that SISR against variations topology STDP parameters. This behavior is opposite presented by Yamakou Jost (Phys. Rev. E 100, 022313, 2019), where parameters but absence STDP. Moreover, increases with a decreasing (increasing) depression temporal window (potentiation adjusting rate) However, poor can significantly enhanced multiplexing another exhibiting or suitable inter-layer parameter values. addition, for all values, enhancement strategy based on occurrence outperforms CR. Finally, optimal occurs via long-term potentiation (long-term depression) synaptic weights.
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ژورنال
عنوان ژورنال: Frontiers in Physics
سال: 2022
ISSN: ['2296-424X']
DOI: https://doi.org/10.3389/fphy.2022.909365