Synchronization in simplicial complexes of memristive Rulkov neurons

Abstract

Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure pairwise hybrid (electrical and chemical) synapses alongside a network with two-node electrical and multi-node chemical connections. The findings provide good insight into the impact of incorporating higher-order interaction in a network. Compared to two-node chemical synapses, higher-order interactions adjust the synchronization patterns to lower multi-node chemical coupling parameter values. Furthermore, the effect of altering higher-order coupling parameter value on the dynamics of neurons in the synchronization state is researched. It is also shown how increasing network size can enhance synchronization by lowering the value of coupling parameters whereby synchronization occurs. Except for complete synchronization, cluster synchronization is detected for higher electrical coupling strength values wherein the neurons are out of the completed synchronization state.

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

“…The relationship between the electromagnetic fields produced near the membrane and the ion flows is crucial for understanding how neurons process information [6]. To explore the irregular firing activities in the nervous system, neuroscientists have proposed artificial neuronal models to simulate and analyze the firing patterns of neurons, such as the HR model [7,8], FitzHugh-Nagumo (FHN) model [9][10][11], Hopfield Neural Network (HNN) model [12][13][14] and others [15,16]. The HR neuron model has attracted much attention, which has simple mathematical expression and rich characterization of electrical activity, and thus has become one of the paradigms of artificial neuron modeling.…”