标题： 异质神经回路中通过反馈抑制的相关性降低 显示英文标题

标题： Decorrelation by recurrent inhibition in heterogeneous neural circuits

摘要： 神经元的活动是相关的，这种相关性影响了大脑处理信息的方式。 我们通过分析一个以强且异质相互作用为特征的网络模型来研究相关性的神经回路机制：兴奋性输入驱动神经活动的波动，这些波动由抑制性反馈所平衡。 特别是，兴奋性输入倾向于使神经元相关联，而抑制性反馈则减少相关性。 我们证明了突触连接的异质性对于这种相关性的抑制是必要的。 我们在无序的突触相互作用上计算统计平均值，并将我们的发现应用于一个简单的线性模型和一个更现实的脉冲网络模型。 我们发现零时间滞后时的相关性为正值，大小为 K^{-1/2}，其中 K 是连接到神经元的连接数。 长时间尺度上的相关性较小，大小为 K^{-1}，这意味着相关性的抑制发生在较短的时间尺度上，约为 K^{-1/2}。 相关性的微小程度与大脑皮层和基底神经节的生理测量结果在定性上是一致的。 该模型可以用于研究以反馈抑制为主的脑区的相关性，例如尾状核和苍白球。 显示英文摘要

摘要： The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity, which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations. We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions, and we apply our findings to both a simple linear model and to a more realistic spiking network model. We find that correlations at zero time-lag are positive and of magnitude K^{-1/2}, where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K^{-1}, implying that inhibition of correlations occurs quickly, on a timescale of K^{-1/2}. The small magnitude of correlations agrees qualitatively with physiological measurements in the Cerebral Cortex and Basal Ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the Striatum and Globus Pallidus.