A neurocomputational model of stochastic resonance and aging
A neurocomputational model of stochastic resonance and aging
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DOI：
10.1016/j.neucom.2005.06.015
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发表时间：
2006-08-01
期刊：
影响因子：
6
通讯作者：
Lindenberger, Ulman
中科院分区：
文献类型：
--
作者：
Li, Shu-Chen;von Oertzen, Timo;Lindenberger, Ulman
随机共振（SR）是物理和生物过程的基础。在这里，我们使用一个随机增益调谐模型来研究与衰老相关的内源性神经元噪声增加和外部输入噪声之间的相互作用，以影响SR。与具有激活函数的最佳系统增益参数的网络相比，在系统水平上具有衰减的内源性增益调谐的网络，模拟具有更多内在神经元噪声但可塑性较低的衰老神经认知系统，继续表现出一般的SR效应;然而，这种效应较小，需要更多的外部噪声。这组发现表明，确定共振诱导外部噪声的最佳比例作为内部系统随机增益调谐特性的函数，促进了在行为和神经分析层面上关于感官和认知老化的统一理论。(c)2005 Elsevier B. V.保留所有权利。
Stochastic resonance (SR) is fundamental to physical and biological processes. Here, we use a stochastic gain-tuning model to investigate interactions between aging-related increase of endogenous neuronal noise and external input noise in affecting SR. Compared to networks that have optimal system gain parameter of the activation function, networks with attenuated endogenous gain tuning at the system level, simulating aging neurocognitive systems with more intrinsic neuronal noise but less plasticity, continue to exhibit the general SR effect; however, this effect is smaller and requires more external noise. This set of finding suggests that determining the optimal proportion of resonance-inducing external noise as a function of internal-system stochastic gain tuning properties promotes unified theorizing about sensory and cognitive aging at behavioral and neural levels of analysis. (c) 2005 Elsevier B.V. All rights reserved.