Asynchronous Rate Chaos in Spiking Neuronal Circuits.
Asynchronous Rate Chaos in Spiking Neuronal Circuits.
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DOI：
10.1371/journal.pcbi.1004266
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发表时间：
2015-07
影响因子：
4.3
通讯作者：
Hansel D
中科院分区：
文献类型：
--
作者：
Harish O;Hansel D
大脑表现出时间复杂的活动模式，其特征类似于混沌系统。过去二十年的理论研究已经描述了神经元系统中这种制度的各种计算优势。尽管如此，目前仍不清楚混沌是否需要特定的细胞特性或网络结构，或者它是否是神经元回路的一般特性。我们研究了具有随机稀疏连接的兴奋-抑制（EI）尖峰神经元网络在兴奋和抑制平衡状态下的动力学。结合动态平均场理论与数值模拟，我们表明，混沌，异步放电率波动出现一般足够强的突触。两种不同的机制可以导致这些混乱的波动。一种机制依赖于缓慢的I-I抑制，其引起缓慢的亚阈值电压和速率波动。这些波动的去相关时间与抑制的时间常数成比例。第二种机制依赖于经常性的E-I-E反馈回路。它需要缓慢的兴奋，但抑制可以很快。在相应的动力学制度，所有的神经元表现出率波动的时间尺度上的激励。该机制的另一个特征是群体平均放电率在兴奋性群体中比在抑制性群体中小得多。在I-I机制中不一定是这种情况。最后，我们讨论了我们的结果的神经生理和计算意义。皮层回路表现出复杂的尖峰时间模式，并且对正在进行的活动中的小扰动非常敏感。这些特征都暗示了潜在的混沌动力学。理论工作已经表明，丰富的动力库可以赋予神经元回路以显著的计算能力。然而，尖峰神经元回路中的混沌机制仍然未知。我们结合联合收割机分析计算和数值模拟来研究这个基本问题。我们的主要结果是，混沌放电率波动的时间尺度上的突触动力学出现一般从网络集体动力学。我们的研究结果为神经网络中混沌态的生理机制和计算意义的研究铺平了道路。
The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. Cortical circuits exhibit complex temporal patterns of spiking and are exquisitely sensitive to small perturbations in their ongoing activity. These features are all suggestive of an underlying chaotic dynamics. Theoretical works have indicated that a rich dynamical reservoir can endow neuronal circuits with remarkable computational capabilities. Nevertheless, the mechanisms underlying chaos in circuits of spiking neurons remain unknown. We combine analytical calculations and numerical simulations to investigate this fundamental issue. Our key result is that chaotic firing rate fluctuations on the time scales of the synaptic dynamics emerge generically from the network collective dynamics. Our results pave the way in the study of the physiological mechanisms and computational significance of chaotic states in neuronal networks.