Deceleration the variables of the discrete recursive model of a neural-like generator to speed up and simplify calculations

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. In this paper, two methods for modifying a new discrete recursive model of a neural-like generator are proposed in order to reduce its computational complexity and decrease the number of control parameters.