真实神经元网络的统计力学

Leenoy Meshulam, William Bialek
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引用次数: 0

摘要

感知和行动、思想和记忆都是大脑中成百上千个神经元协调活动的结果。为这些和其他生命现象提供统计力学描述是物理学界的一个古老梦想。由于我们测量脑电活动的能力不断发展,我们可以在数小时或数天内同时对数千个神经元进行采样,因此这些愿望有了新的曙光。我们回顾了将理论与实验结合起来所取得的进展,重点是最大熵方法和现象学重正化群。这些方法在真实神经元网络中发现了新的、可定量重现的集体行为,并提供了丰富的无参数预测实例,这些预测与实验的细节相吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical mechanics for networks of real neurons
Perceptions and actions, thoughts and memories result from coordinated activity in hundreds or even thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics description for these and other emergent phenomena of life. These aspirations appear in a new light because of developments in our ability to measure the electrical activity of the brain, sampling thousands of individual neurons simultaneously over hours or days. We review the progress that has been made in bringing theory and experiment together, focusing on maximum entropy methods and a phenomenological renormalization group. These approaches have uncovered new, quantitatively reproducible collective behaviors in networks of real neurons, and provide examples of rich parameter--free predictions that agree in detail with experiment.
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