https://doi.org/10.1140/epjs/s11734-025-01987-0
Regular Article
The firing transition and its consumed energy optimized by adaptive modulation of memristive synapses gain in the cat brain neural network
1
College of Physics and Electronic Information, Anhui Normal University, 241000, Wuhu, China
2
School of Medical Imageology, Wan Nan Medical College, 241000, Wuhu, China
Received:
20
February
2025
Accepted:
23
September
2025
Published online:
20
October
2025
To study both the relationship and law of the transition of firing modes and energy evolution in brain neural networks, we construct a biologically plausible neural network based on neuroanatomical data of cat cortical connectivity. By using the network coarsening theory, each cortex is coarsened into a network node, and the memristor Hindmarsh–Rose neuron models are used to replace these nodes. Firstly, through adaptive regulation of memristive synapses’ gain, the transitions between different firing modes, including chimera state, synchronous state, and others can be achieved. The results demonstrate that adaptive memristive synapses can effectively achieve dynamic control over collective neuronal activity patterns; thereby, significantly enhancing the network's capacity for state transitions. Furthermore, we systematically analyze Hamilton energy dynamics during transitions among different characteristic firing states. The phase diagram analysis indicates that the energy required is the lowest during the state transition in the synchronous modes, implying the energy-optimizing role of adaptive regulation. This result provides novel insights into the adaptive synchronous dynamics in neural networks and provides a mechanistic framework for understanding neurological disorders such as epilepsy. Additionally, the above work establishes a theoretical foundation for developing more efficient and energy-saving neuromorphic computing systems based on memristive technologies.
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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.
