Humble James
Independent Researcher, Randolph, MA, United States.
Front Comput Neurosci. 2025 Jul 22;19:1593837. doi: 10.3389/fncom.2025.1593837. eCollection 2025.
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 STDP-based 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.
许多纳入了依赖于脉冲时间的可塑性（STDP）的计算模型都显示出了从刺激中学习的能力，支持了STDP是学习和记忆的充分基础的理论。然而，为了防止失控的活动和增强，特别是在循环网络中，通常还需要额外的全局机制。一种基于STDP的学习规则，涉及局部资源依赖的增强和异突触抑制，被证明能够在循环脉冲网络中实现稳定学习。增强和抑制之间的平衡促进了突触稳态，并且学习到的突触特征与实验观察结果一致。此外，这种基于资源的STDP学习规则展示了一种针对突触退化的固有补偿机制。
