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具有局部抑制和长程兴奋的神经网络中集体振荡的时间去相关

Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation.

作者信息

Battaglia Demian, Brunel Nicolas, Hansel David

机构信息

Université Paris Descartes, Laboratoire de Neurophysique et Physiologie; CNRS UMR 8119; 45, Rue des Saints-Pères, 75270 Paris Cedex 06, France.

出版信息

Phys Rev Lett. 2007 Dec 7;99(23):238106. doi: 10.1103/PhysRevLett.99.238106.

Abstract

We consider two neuronal networks coupled by long-range excitatory interactions. Oscillations in the gamma frequency band are generated within each network by local inhibition. When long-range excitation is weak, these oscillations phase lock with a phase shift dependent on the strength of local inhibition. Increasing the strength of long-range excitation induces a transition to chaos via period doubling or quasiperiodic scenarios. In the chaotic regime, oscillatory activity undergoes fast temporal decorrelation. The generality of these dynamical properties is assessed in firing-rate models as well as in large networks of conductance-based neurons.

摘要

我们考虑两个通过长程兴奋性相互作用耦合的神经网络。每个网络内部通过局部抑制产生伽马频段的振荡。当长程兴奋性较弱时,这些振荡会以取决于局部抑制强度的相移进行锁相。增加长程兴奋性的强度会通过倍周期或准周期情形诱导向混沌的转变。在混沌状态下,振荡活动会经历快速的时间去相关。这些动力学特性的普遍性在发放率模型以及基于电导的神经元大网络中得到了评估。

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