How synaptic plasticity affects the stochastic resonance in a modular neuronal network

This work mainly investigates how synaptic plasticity influences the stochastic resonance dynamics of delay-coupled FitzHugh–Nagumo neurons in a modular neuronal network subjected to bounded noise. This modular neuronal network is consisted of two small-world subnetworks. The couplings between different neurons are of synaptic plasticity, which is regulated by a modified Oja’s learning rule. Numerical results show that the classical phenomenon of stochastic resonance can occur at an intermediate amplitude of bounded noise on this network. Also, the result reveals that appropriately tuned delays can induce multiple stochastic resonances, appearing at every integer multiple of the period of weak period signal. Moreover, when incorporating synaptic plasticity into this modular neuronal network, we find that the bounded noise-induced stochastic resonance is almost quantitatively unchanged upon increasing synaptic learning rate, i.e., the phenomenon is robust to the synaptic plasticity. Interestingly, the delay-induced multiple stochastic resonances are slightly restrained by the increase in synaptic learning rate. These present findings could be helpful to understand the important role of synaptic plasticity on neural coding in realistic neuronal network.