The correlation structure of local neuronal networks intrinsically results from recurrent dynamics.
The correlation structure of local neuronal networks intrinsically results from recurrent dynamics.
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DOI：
10.1371/journal.pcbi.1003428
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发表时间：
2014-01
影响因子：
4.3
通讯作者：
Diesmann M
中科院分区：
文献类型：
--
作者：
Helias M;Tetzlaff T;Diesmann M
相关的神经元活动是网络连接和对神经元的共享输入的自然结果，但是与行为相关的相关性的任务依赖性调制也暗示了功能性作用。相关性影响突触后神经元的增益、群体活动中编码的信息量和读出神经元解码的信息量以及突触可塑性。此外，它还影响细胞外信号的功率和空间范围，如局部场电位。目前缺乏一个理论的相关神经元活动占经常性的连接以及波动的外部来源。特别是，目前还不清楚最近发现的机制，积极去相关的负反馈的人口水平影响网络响应外部施加的相关刺激。在这里，我们提出了这样一个扩展的随机二元网络的相关性理论。我们发现：（1）对于均匀的外部输入，相关的结构主要由局部递归连接决定，（2）均匀的外部输入提供了一个附加的，非特异性的相关贡献，（3）抑制反馈有效地去相关神经元的活动，即使神经元接收相同的外部输入，（4）兴奋性细胞和抑制性细胞的突触输入统计相同，增加了内在产生的波动和成对相关性。我们进一步展示了如何通过自洽包括相关性来提高平均场预测的准确性。作为一个副产品，我们表明，取消之间的相关性的总和输入对神经元并不源于外部输入的快速跟踪，但从抑制的波动人口水平的本地网络。这种抑制是一个必要的约束，但不足以确定相关性的结构;具体来说，在有限的网络大小观察到的结构不同于基于完美跟踪的预测，即使完美跟踪意味着抑制人口波动。在短时间间隔内神经元对的动作电位的共同出现已经知道很长时间了。这种同步事件可能会出现时间锁定的动物的行为，也理论上的考虑认为同步的功能作用。早期的理论工作试图解释神经元由于共享输入而传递共同波动的相关活动。然而，这高估了相关性。最近，皮层网络的经常性连接被证明是所观察到的低基线相关性的原因。有两种不同的解释：一种认为，兴奋性和抑制性群体活动密切关注网络的外部输入，因此它们对一对细胞的影响相互抵消。另一种解释依赖于负的经常性反馈来抑制种群活动的波动，相当于小的相关性。在生物神经元网络中，人们期望外部输入和重现两者都影响相关的活动。目前的工作扩展了相关性的理论框架，包括两个贡献，并解释了它们的质的差异。此外，研究表明，快速跟踪和经常性反馈的论点是不等价的，只有后者正确预测细胞类型的特定相关性。
Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of information encoded in the population activity and decoded by readout neurons, and synaptic plasticity. Further, it affects the power and spatial reach of extracellular signals like the local-field potential. A theory of correlated neuronal activity accounting for recurrent connectivity as well as fluctuating external sources is currently lacking. In particular, it is unclear how the recently found mechanism of active decorrelation by negative feedback on the population level affects the network response to externally applied correlated stimuli. Here, we present such an extension of the theory of correlations in stochastic binary networks. We show that (1) for homogeneous external input, the structure of correlations is mainly determined by the local recurrent connectivity, (2) homogeneous external inputs provide an additive, unspecific contribution to the correlations, (3) inhibitory feedback effectively decorrelates neuronal activity, even if neurons receive identical external inputs, and (4) identical synaptic input statistics to excitatory and to inhibitory cells increases intrinsically generated fluctuations and pairwise correlations. We further demonstrate how the accuracy of mean-field predictions can be improved by self-consistently including correlations. As a byproduct, we show that the cancellation of correlations between the summed inputs to pairs of neurons does not originate from the fast tracking of external input, but from the suppression of fluctuations on the population level by the local network. This suppression is a necessary constraint, but not sufficient to determine the structure of correlations; specifically, the structure observed at finite network size differs from the prediction based on perfect tracking, even though perfect tracking implies suppression of population fluctuations. The co-occurrence of action potentials of pairs of neurons within short time intervals has been known for a long time. Such synchronous events can appear time-locked to the behavior of an animal, and also theoretical considerations argue for a functional role of synchrony. Early theoretical work tried to explain correlated activity by neurons transmitting common fluctuations due to shared inputs. This, however, overestimates correlations. Recently, the recurrent connectivity of cortical networks was shown responsible for the observed low baseline correlations. Two different explanations were given: One argues that excitatory and inhibitory population activities closely follow the external inputs to the network, so that their effects on a pair of cells mutually cancel. Another explanation relies on negative recurrent feedback to suppress fluctuations in the population activity, equivalent to small correlations. In a biological neuronal network one expects both, external inputs and recurrence, to affect correlated activity. The present work extends the theoretical framework of correlations to include both contributions and explains their qualitative differences. Moreover, the study shows that the arguments of fast tracking and recurrent feedback are not equivalent, only the latter correctly predicts the cell-type specific correlations.
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影响因子：
13.9
作者：
Buzsáki G;Wang XJ
通讯作者：
Wang XJ
影响因子：
3.5
作者：
Hanuschkin A;Kunkel S;Helias M;Morrison A;Diesmann M
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影响因子：
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作者：
Hertz, John
通讯作者：
Hertz, John
影响因子：
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作者：
GINZBURG, I;SOMPOLINSKY, H
通讯作者：
SOMPOLINSKY, H
影响因子：
3.3
作者：
Helias, M.;Tetzlaff, T.;Diesmann, M.
通讯作者：
Diesmann, M.