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局部皮质回路中的同步混沌

Synchronized chaos in local cortical circuits.

作者信息

Hansel D

机构信息

Centre de Physique Théorique, UPR014-CNRS, Ecole Polytechnique, Palaiseau, France.

出版信息

Int J Neural Syst. 1996 Sep;7(4):403-15. doi: 10.1142/s0129065796000385.

Abstract

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This is in marked contrast to the behavior of cortical neurons in-vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a "hypercolumn" in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model with several cellular and synaptic conductances. The pattern of connectivity is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and analyzed. The relation between the results of the model and experiments in visual cortex is discussed.

摘要

皮层切片中的神经元在受到阈上电流注入时会定期发放单个尖峰或尖峰簇。这与皮层神经元在体内的行为形成了显著对比,体内的皮层神经元对电输入或感觉输入的反应表现出很强的不规则性。相关性测量表明,皮层中神经元活动的时间波动存在显著程度的同步性。我们探讨了这样一种假设,即这些现象是由局部皮层网络的确定性动力学产生的同步混沌的结果。对视觉皮层中的一个“超柱”模型进行了研究。它由两类神经元群体组成,一类是抑制性的,另一类是兴奋性的。神经元的动力学基于一个具有多种细胞和突触电导的霍奇金 - 赫胥黎类型模型。连接模式与定向柱中超柱的内部组织相关。该模型的数值模拟表明,在适当的参数范围内,网络会进入一种同步混沌状态,其特征是神经活动具有很强的时间变异性,且在整个超柱中相关。强抑制性反馈对于这种状态的稳定至关重要。计算并分析了神经元尖峰序列的自相关和互相关函数。讨论了模型结果与视觉皮层实验之间的关系。

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