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Neural networks of different species, brain areas and states can be characterized by the probability polling state
European Journal of Neuroscience ( IF 2.7 ) Pub Date : 2020-06-13 , DOI: 10.1111/ejn.14860
Zhi‐Qin John Xu 1 , Xiaowei Gu 2, 3 , Chengyu Li 2 , David Cai 1, 4 , Douglas Zhou 1 , David W. McLaughlin 4
Affiliation  

Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical neural networks function, it is important to identify their common dynamical operating states from the probabilistic viewpoint. Probabilistic characteristics of these operating states often underlie network functions. Here, using multi‐electrode data from three separate experiments, we identify and characterize a cortical operating state (the “probability polling” or “p‐polling” state), common across mouse and monkey with different behaviors. If the interaction among neurons is weak, the p‐polling state provides a quantitative understanding of how the high dimensional probability distribution of firing patterns can be obtained by the low‐order maximum entropy formulation, effectively utilizing a low dimensional stimulus‐coding structure. These results show evidence for generality of the p‐polling state and in certain situations its advantage of providing a mathematical validation for the low‐order maximum entropy principle as a coding strategy.

中文翻译:

不同物种,大脑区域和状态的神经网络可以通过概率轮询状态来表征

皮质网络是由许多相互连接的神经元组成的复杂系统,这些神经元从集体动力学状态运行。要了解皮质神经网络的功能,从概率角度确定它们的常见动态运行状态非常重要。这些操作状态的概率特征通常是网络功能的基础。在这里,我们使用来自三个独立实验的多电极数据,来识别和表征皮质行为状态(“概率轮询”或“ p轮询”状态),这种行为在具有不同行为的小鼠和猴子中很常见。如果神经元之间的相互作用较弱,则p轮询状态可以定量地了解如何通过低阶最大熵公式获得点火模式的高维概率分布,有效地利用低维刺激编码结构。这些结果证明了p轮询状态的普遍性,并在某些情况下为低阶最大熵原理提供了一种数学验证作为编码策略的优势。
更新日期:2020-07-13
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