Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL 60637, USA.
Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA.
Sci Adv. 2024 Oct 18;10(42):eadl6120. doi: 10.1126/sciadv.adl6120. Epub 2024 Oct 16.
A core problem in systems and circuits neuroscience is deciphering the origin of shared dynamics in neuronal activity: Do they emerge through local network interactions, or are they inherited from external sources? We explore this question with large-scale networks of spatially ordered spiking neuron models where a downstream network receives input from an upstream sender network. We show that linear measures of the communication between the sender and receiver networks can discriminate between emergent or inherited population dynamics. A match in the dimensionality of the sender and receiver population activities promotes faithful communication. In contrast, a nonlinear mapping between the sender to receiver activity, for example, through downstream emergent population-wide fluctuations, can impair linear communication. Our work exposes the benefits and limitations of linear measures when analyzing between-area communication in circuits with rich population-wide neuronal dynamics.
它们是通过局部网络相互作用产生的，还是从外部来源继承的？我们通过具有空间有序的尖峰神经元模型的大规模网络来探索这个问题，其中下游网络接收来自上游发送者网络的输入。我们表明，发送者和接收者网络之间的通信的线性度量可以区分出出现的或继承的群体动力学。发送者和接收者群体活动的维度匹配促进了忠实的通信。相比之下，发送者到接收者活动之间的非线性映射，例如，通过下游出现的全种群波动，可以损害线性通信。我们的工作揭示了在具有丰富的全种群神经元动力学的电路中分析区域间通信时，线性测量的优势和局限性。
