A Computational Model of Neuroreceptor-Dependent Plasticity (NRDP) Based on Spiking Neural Networks

Activity-dependent plasticity has attracted the interest of researchers for years in the domain of computational neuroscience, as the modification of synaptic efficacy occurs as a result of complex biochemical mechanisms that take place at a cellular level. In this paper, we introduce a phenomenological model implemented as an unsupervised learning rule for spiking neural networks (SNNs) based on the cross-talk between glutamatergic and ${\gamma }$ -aminobutyric acid (GABA)ergic neuroreceptors: ${N}$ -methyl- ${D}$ -aspartate receptor, ${\alpha }$ -amino-3-hydroxy-5-methyl-4-izoxazole-propionic acid receptors, GABA _A , and GABA _B . The proposed neuroreceptor-dependent plasticity (NRDP) model is implemented and demonstrated in an SNN environment, NeuCube, for modeling electroencephalography data. We show that the NRDP model can reproduce the generic spike-timing dependent plasticity behavior in an SNN. In addition, this can be used to simulate changes in excitatory/inhibitory balance in an SNN by altering neuroreceptors activity. More specifically, by varying the parameters that affect neuroreceptors activation, we can study how these changes would affect the learning and memory ability of a subject. In a therapeutic context, this makes it a promising tool for studying the regulatory mechanisms, where neuroreceptors cross-talk plays a crucial role. This can lead to new ways of early detection of neurological disorders and for better targeting drug treatments.