BRIEF RESEARCH REPORT article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1593837
This article is part of the Research TopicTheory and Models of Synaptic PlasticityView all 4 articles
Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration
Provisionally accepted
- Independent Researcher, Randolph, United States
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Many computational models that incorporate spike-timing-dependent plasticity (STDP) have shown the ability to learn from stimuli, supporting theories that STDP is a sufficient basis for learning and memory. However, to prevent runaway activity and potentiation, particularly within recurrent networks, additional global mechanisms are commonly necessary. A STDPbased learning rule, which involves local resource-dependent potentiation and heterosynaptic depression, is shown to enable stable learning in recurrent spiking networks. A balance between potentiation and depression facilitates synaptic homeostasis, and learned synaptic characteristics align with experimental observations. Furthermore, this resource-based STDP learning rule demonstrates an innate compensatory mechanism for synaptic degeneration.
Keywords: Spike-timing-dependent plasticity, Homeostasis, heterosynaptic, Recurrent network, Learning, spiking, synaptic degeneration, neurodegeneration
Received: 14 Mar 2025; Accepted: 19 May 2025.
Copyright: © 2025 Humble. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: James Humble, Independent Researcher, Randolph, United States
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