自适应小世界神经网络中的逆随机共振

Marius E. Yamakou, Jinjie Zhu, Erik A. Martens
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摘要

反向随机共振(ISR)是指噪声降低而不是增加神经元的发射率,有时会导致完全静止的现象。ISR 首次在小脑珀金神经元上得到实验验证。这些实验表明,ISR 使神经元的输入和输出尖峰序列之间实现了最佳的信息传递。随后的研究证明了具有小世界拓扑结构的神经网络中信息处理和传递的效率。我们对一个由噪声菲茨休-纳古莫(FHN)神经元组成的小世界网络中适应性对 ISR 的影响进行了数值研究,该网络运行在双稳态系统中,具有稳定的固定点和极限周期(ISR 的先决条件)。我们的研究结果表明,ISR的程度高度依赖于FHN模型的时标分离参数$\epsilon$。网络结构通过尖峰时间相关可塑性(STDP)和同态结构可塑性(HSP)两种机制进行动态适应,前者的强直-/抑制-定标参数为$P$,后者的重布线频率为$F$。我们证明,当$epsilon$位于FHN神经元的双稳态区域内时,STDP和HSP都会放大ISR。具体来说,当双稳态区内的$epsilon$值较大时,较高的重布线频率$F$会在中等(弱)突触噪声强度下增强ISR,而与抑制-支配(电位-支配)一致的$P$值则会增强(恶化)ISR。我们的发现为未来噪声人工神经回路的ISR增强策略提供了参考,旨在优化神经形态系统中输入和输出尖峰列车之间的信息传递,并为神经网络实验提供了新的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inverse stochastic resonance in adaptive small-world neural networks
Inverse stochastic resonance (ISR) is a phenomenon where noise reduces rather than increases the firing rate of a neuron, sometimes leading to complete quiescence. ISR was first experimentally verified with cerebellar Purkinje neurons. These experiments showed that ISR enables optimal information transfer between the input and output spike train of neurons. Subsequent studies demonstrated the efficiency of information processing and transfer in neural networks with small-world topology. We conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bistable regime with a stable fixed point and a limit cycle -- a prerequisite for ISR. Our results show that the degree of ISR is highly dependent on the FHN model's timescale separation parameter $\epsilon$. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter $P$, or homeostatic structural plasticity (HSP) with rewiring frequency $F$. We demonstrate that both STDP and HSP amplify ISR when $\epsilon$ lies within the bistability region of FHN neurons. Specifically, at larger values of $\epsilon$ within the bistability regime, higher rewiring frequencies $F$ enhance ISR at intermediate (weak) synaptic noise intensities, while values of $P$ consistent with depression-domination (potentiation-domination) enhance (deteriorate) ISR. Moreover, although STDP and HSP parameters may jointly enhance ISR, $P$ has a greater impact on ISR compared to $F$. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize information transfer between input and output spike trains in neuromorphic systems, and prompt venues for experiments in neural networks.
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