Department of Physics, Ecole Normale Supérieure, Paris, France.
Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité, Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France.
PLoS Comput Biol. 2018 Feb 6;14(2):e1005979. doi: 10.1371/journal.pcbi.1005979. eCollection 2018 Feb.
Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.
神经群体对重复呈现的感觉刺激会产生相关的可变性。人们已经详细研究了这些相关性，包括它们的机械起源，以及它们对刺激辨别和群体编码性能的影响。许多理论研究都试图将网络结构与神经活动中的相关性本质联系起来。在这里，我们为此做出了贡献：在随机神经元电路模型中，我们阐明了各种网络结构——递归连接、共享前馈投射和共享增益波动——对相关性中刺激依赖的影响。具体来说，我们推导出了数学关系，这些关系指定了群体平均协方差与放电率之间的关系，适用于不同的网络结构。反过来，这些关系可以用于分析群体活动数据。我们检查了来自小鼠听觉皮层神经群体的记录。我们发现，带有随机有效连接的递归网络模型可以捕捉到观察到的统计数据。此外，我们使用电路模型研究了网络参数、相关性以及基于群体活动，不同刺激之间的可辨别程度之间的关系。因此，我们的方法使我们能够将神经电路的特性与信息处理联系起来。
