https://doi.org/10.1140/epjs/s11734-025-01942-z
Regular Article
Discrete recursive map neuron model
National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
Received:
14
July
2025
Accepted:
7
September
2025
Published online:
15
September
2025
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 implementation 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.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
