标题： 真实神经元网络的统计力学 显示英文标题

标题： Statistical mechanics for networks of real neurons

摘要： 感知和行为、思想和记忆源于大脑中数百甚至数千个神经元的协调活动。 物理界有一个古老的梦想，即为这些以及其他生命中的涌现现象提供统计力学描述。 由于我们测量大脑电活动的能力有了进步，在数小时或数天内同时采样数千个单独神经元，这些愿望呈现出新的视角。 我们回顾了理论和实验相结合所取得的进展，重点介绍了最大熵方法和现象学重正化群。 这些方法揭示了真实神经网络中新的、可定量重复的集体行为，并提供了与实验细节相符的丰富无参数预测例子。 显示英文摘要

摘要： 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.