Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany.
State Key Laboratory of Mechanics and Control for Aerospace Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Chaos. 2024 Nov 1;34(11). doi: 10.1063/5.0225760.
Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have 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 bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. 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 the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.
反随机共振（ISR）是一种反直觉的现象，即在噪声下振荡器的振荡频率会降低到一个最小值，这个最小值出现在中等强度的噪声下，有时甚至完全没有振荡。在神经科学中，小脑浦肯野神经元的实验首次验证了 ISR[Buchin 等人，PLOS Comput. Biol. 12, e1005000 (2016)]。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局部最优的信息传递。随后的研究进一步证明了具有小世界网络拓扑的神经网络中的信息处理和传递效率。我们对噪声菲茨休-纳格姆（FHN）神经元小世界网络中的适应性对 ISR 的影响进行了数值研究，该网络处于由亚稳定固定点和亚稳定极限环组成的双亚稳态。我们的结果表明，ISR 的程度高度依赖于 FHN 模型的时间尺度分离参数 ε 的值。网络结构通过尖峰时间依赖可塑性（STDP）的机制或具有加/减主导参数 P 的稳态结构可塑性（HSP）进行动态适应，或具有重连频率 F 的稳态结构可塑性（HSP）进行动态适应。我们证明，当 ε 处于 FHN 神经元的双稳区时，STDP 和 HSP 都会放大 ISR 的效应。具体来说，在双稳区的较大 ε 值范围内，观察到更高的重连频率 F 在中等（弱）突触噪声强度下增强 ISR，而与减主导（加主导）一致的 P 值始终增强（恶化）ISR。此外，尽管 STDP 和 HSP 控制参数可能共同增强 ISR，但 P 对改善 ISR 的影响大于 F。我们的发现为嘈杂人工神经网络中的未来 ISR 增强策略提供了信息，旨在优化神经形态系统中输入和输出尖峰序列之间的局部信息传递，并为神经网络中的实验提供了场所。
