Recursive map neuron model
Denis I. Bolshakov, Mikhail A. Mishchenko, Daniil V. Chindarev, Valery V. Matrosov · 2024
Study of spiking neural networks is a promising scientific direction of modern interdisciplinary research. Spiking networks have demonstrated high efficacy in processing and classification tasks on various datasets (pictures, acoustic signals, biological signals) and in robotics (navigation, movement control, interaction with media etc.) One of the main problems of this type of neural networks is high computational and implementational cost. This problem rises from computational complexity of many nonlinearities in neuronal models and nonlinear synaptic functions. Novel low-computational-cost models of spiking neurons and synapses could be a possible decision of this problem. Here, we propose a new discrete recursive neuron model. The model demonstrates rich spiking and bursting dynamic repertoire and requires relatively small computational resources. Moreover, the proposed model could be implemented by standard discrete logic elements.