Optimal Sequence Memory in Driven Random Networks
Optimal Sequence Memory in Driven Random Networks
复制标题
驱动随机网络中的最优序列记忆
作者：
Jannis Schuecker;Sven Goedeke;M. Helias
自治随机耦合神经网络在一个临界耦合强度下表现出向混沌的过渡。在这里，我们调查的效果随时间变化的输入混沌的发病和由此产生的后果，信息处理。动态平均场理论产生的活动，最大李雅普诺夫指数，和网络的记忆容量的统计。我们找到了一个精确的条件，确定从稳定到混沌动力学的过渡和封闭形式的顺序记忆容量。输入抑制混沌的动力学机制，转移到显着更大的耦合强度比预测的局部稳定性分析。除了线性稳定性之外，还出现了一种共存的局部扩张但非混沌的动态机制，它优化了网络存储序列输入的能力。
Autonomous randomly coupled neural networks display a transition to chaos at a critical coupling strength. We here investigate the effect of a time-varying input on the onset of chaos and the resulting consequences for information processing. Dynamic mean-field theory yields the statistics of the activity, the maximum Lyapunov exponent, and the memory capacity of the network. We find an exact condition that determines the transition from stable to chaotic dynamics and the sequential memory capacity in closed form. The input suppresses chaos by a dynamic mechanism, shifting the transition to significantly larger coupling strengths than predicted by local stability analysis. Beyond linear stability, a regime of coexistent locally expansive, but non-chaotic dynamics emerges that optimizes the capacity of the network to store sequential input.
影响因子：
2.4
作者：
Rajan, Kanaka;Abbott, L. F.;Sompolinsky, Haim
通讯作者：
Sompolinsky, Haim