Stimulus-dependent suppression of chaos in recurrent neural networks
Stimulus-dependent suppression of chaos in recurrent neural networks
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DOI：
10.1103/physreve.82.011903
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发表时间：
2010-07-07
影响因子：
2.4
通讯作者：
Sompolinsky, Haim
中科院分区：
文献类型：
--
作者：
Rajan, Kanaka;Abbott, L. F.;Sompolinsky, Haim
神经元活动是由神经回路自发产生的持续放电和外部刺激驱动的反应之间的相互作用引起的。使用平均场分析，我们问一个神经网络，本质上产生混沌模式的活动可以保持敏感的外部输入。我们发现，输入不仅驱动网络响应，而且还积极抑制正在进行的活动，最终导致混沌完全消除的相变。相变时的临界输入强度是激励频率的非单调函数，揭示了一个“共振”频率，在该频率下，即使自发活动的功率谱在零处达到峰值并呈指数福尔斯下降，输入也能最有效地抑制混沌。我们的分析预测是，神经反应的方差应该在与许多感觉系统操作的范围相匹配的频率处被最强烈地抑制。
Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a "resonant" frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.