Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks
Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks
复制标题
临界平衡神经元网络的自发和刺激诱导的相干状态
DOI：
10.1103/physrevresearch.2.013253
复制
发表时间：
2020
影响因子：
4.2
通讯作者：
Tomoki Fukai
中科院分区：
文献类型：
--
作者：
Takashi Hayakawa;Tomoki Fukai
如何整合单个神经元微观处理的信息并用于组织动物的行为是神经科学的核心问题。不同尺度上神经元动力学的一致性被认为是这种整合背后机制的线索。平衡的强激发和抑制可以将微观波动放大到宏观水平，从而提供产生连贯的多尺度神经元动力学的机制。然而，先前的大脑动力学理论仅限于抑制主导兴奋并抑制宏观群体活动波动的情况。在本研究中，我们研究了兴奋主导状态和抑制主导状态之间关键点的神经元网络动力学。在这些网络中，神经元活动的微观波动被强烈的兴奋和抑制放大，驱动宏观动力学，而宏观动力学决定微观波动的统计。我们开发了一种适用于这类尺度间相互作用的新型平均场理论，这种相互作用的分析方法以前是未知的，我们证明放大机制会产生自发的、不规则的宏观节律，类似于在大脑中观察到的节律。通过相同的机制，少量神经元的微观输入有效地带动了整个网络的动态。随着平衡激励和抑制或外部输入的幅度增加，这些网络动力学经历到相干状态的概率转变。我们的平均场理论成功地预测了该模型的行为。此外，我们通过数值证明相干动力学可用于从网络中读取与状态相关的信息。这些结果展示了一种新颖的神经元信息处理形式，它在不同尺度上连接神经元动力学，增进了我们对大脑计算电路机制的理解。
How the information microscopically processed by individual neurons is integrated and used in organizing the behavior of an animal is a central question in neuroscience. The coherence of neuronal dynamics over different scales has been suggested as a clue to the mechanisms underlying this integration. Balanced strong excitation and inhibition may amplify microscopic fluctuations to a macroscopic level, thus providing a mechanism for generating coherent multiscale neuronal dynamics. Previous theories of brain dynamics, however, were restricted to cases in which inhibition dominated excitation and suppressed fluctuations in the macroscopic population activity. In the present study, we investigate the dynamics of neuronal networks at a critical point between excitation-dominant and inhibition-dominant states. In these networks, the microscopic fluctuations in neuronal activities are amplified by the strong excitation and inhibition to drive the macroscopic dynamics, while the macroscopic dynamics determine the statistics of the microscopic fluctuations. Developing a novel type of mean-field theory applicable to this class of interscale interactions, for which an analytical approach has previously been unknown, we show that the amplification mechanism generates spontaneous, irregular macroscopic rhythms similar to those observed in the brain. Through the same mechanism, microscopic inputs to a small number of neurons effectively entrain the dynamics of the whole network. These network dynamics undergo a probabilistic transition to a coherent state, as the magnitude of either the balanced excitation and inhibition or the external inputs is increased. Our mean-field theory successfully predicts the behavior of this model. Furthermore, we numerically demonstrate that the coherent dynamics can be used for state-dependent read-out of information from the network. These results show a novel form of neuronal information processing that connects neuronal dynamics on different scales, advancing our understanding of the circuit mechanisms of brain computing.
登录
查看更多内容
影响因子：
2.4
作者：
GINZBURG, I;SOMPOLINSKY, H
通讯作者：
SOMPOLINSKY, H
影响因子：
4.6
作者：
Shukla N;Parihar A;Freeman E;Paik H;Stone G;Narayanan V;Wen H;Cai Z;Gopalan V;Engel-Herbert R;Schlom DG;Raychowdhury A;Datta S
通讯作者：
Datta S
神经元对中突触之间的相关性会减慢随机连接的神经网络中的动力学。
DOI：
10.1103/physreve.97.062314
发表时间：
2017
期刊：
Physical review. E
影响因子：
--
作者：
Daniel Martí;N. Brunel;S. Ostojic
通讯作者：
S. Ostojic
影响因子：
56.9
作者：
V. Gates;.. M. L. Ludwig-M.-L.-Ludwig-2273875465;J. A. Hartsuck;T. A. Steitz;H. Muirhead;J. C. Coppola;G. N. Reeke
通讯作者：
V. Gates;.. M. L. Ludwig-M.-L.-Ludwig-2273875465;J. A. Hartsuck;T. A. Steitz;H. Muirhead;J. C. Coppola;G. N. Reeke
驱动随机网络中的最优序列记忆
影响因子：
12.5
作者：
Jannis Schuecker;Sven Goedeke;M. Helias
通讯作者：
M. Helias