输入相关性对平衡速率网络混沌抑制和学习的影响
Input correlations impede suppression of chaos and learning in balanced rate networks
摘要：
神经回路表现出复杂的活动模式，既有自发的，也有由外部刺激引起的。神经回路中的信息编码和学习取决于时变刺激对自发网络活动的控制程度。我们表明，在平衡状态下的发射率网络，外部控制的经常性动态，即，内部产生的混沌可变性的抑制强烈地依赖于输入中的相关性。平衡网络的一个独特之处在于，由于共同的外部输入被递归反馈动态地抵消，因此，通过每个神经元的独立输入比通过共同输入更容易抑制混沌。为了研究这一现象，我们开发了一个非平稳动态平均场理论，确定如何活动统计和最大李雅普诺夫指数依赖于频率和振幅的输入，经常性耦合强度，和网络的大小，共同和独立的输入。我们还表明，不相关的输入有利于学习平衡网络。
英文摘要：
Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A unique feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far easier to suppress chaos with independent inputs into each neuron than through common input. To study this phenomenon we develop a non-stationary dynamic mean-field theory that determines how the activity statistics and largest Lyapunov exponent depend on frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We also show that uncorrelated inputs facilitate learning in balanced networks.
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