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具有突触可塑性的兴奋性神经网络中的混沌与相关雪崩

Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity.

作者信息

Pittorino Fabrizio, Ibáñez-Berganza Miguel, di Volo Matteo, Vezzani Alessandro, Burioni Raffaella

机构信息

Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, via G.P. Usberti, 7/A-43124 Parma, Italy.

INFN, Gruppo Collegato di Parma, via G.P. Usberti, 7/A-43124 Parma, Italy.

出版信息

Phys Rev Lett. 2017 Mar 3;118(9):098102. doi: 10.1103/PhysRevLett.118.098102.

Abstract

A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

摘要

在具有短期突触可塑性的纯兴奋性漏电积分发放神经元的无序平均场网络中,观察到活动事件具有幂律标度的集体混沌阶段。动力学相图展示了从准同步和异步状态到具有雪崩的非平凡、集体、爆发状态的两个转变。在没有无序的均匀情况下,系统会同步,并且对于二维离散映射,爆发行为会反映在通向混沌的倍周期转变中。数值模拟表明,具有雪崩的爆发混沌阶段表现出持久时间相关性的自发出现以及增强的柯尔莫哥洛夫复杂性。我们的分析揭示了一种由无序和确定性潜在混沌动力学相结合而产生不规则雪崩的机制。

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