Montani Fernando, Ince Robin A A, Senatore Riccardo, Arabzadeh Ehsan, Diamond Mathew E, Panzeri Stefano
Robotics, Brain, and Cognitive Sciences Department, Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy.
Philos Trans A Math Phys Eng Sci. 2009 Aug 28;367(1901):3297-310. doi: 10.1098/rsta.2009.0082.
Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local population of neurons, a macroscopic index of the activation of the neural network that can be measured experimentally. We analyse a model based on physics' maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real neural population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the neural population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of neural networks, and that they should be taken into account when computing the representational capacity of neural populations.
要理解大脑中神经网络的运作，需要了解神经元之间的相互作用是否可以用成对相互作用模型来描述，或者是否需要更高阶的相互作用模型。在本文中，我们考虑局部神经元群体的同步放电速率，这是一个可以通过实验测量的神经网络激活的宏观指标。我们分析了一个基于物理学最大熵原理的模型，该模型评估同步放电的概率是否可以用任意给定阶数的相互作用来描述。与从大鼠体感皮层获得的真实神经群体活动相比，该模型表明至少需要三阶或四阶相互作用来解释数据。我们使用香农信息来计算高阶相关性对通过同步放电速率传输的体感信息量的影响，并且我们发现高阶相关性会逐渐减少通过神经群体可获得的信息。这些结果与高阶相互作用在塑造神经网络动力学中起作用的假设相一致，并且在计算神经群体的表征能力时应该考虑到它们。
