Simple input–output dependencies explain neuronal activity

doi:10.1038/s41567-026-03306-3

2026-05-18

Christopher W. Lynn *

DOI：10.1038/s41567-026-03306-3

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摘要

En 中文

我们对神经计算的理解建立在神经元对输入线性求和做出反应的假设之上。然而，实验表明，一些神经元能够执行需要输入间交互的复杂功能。这里我们证明，直接依赖性——无需输入间的交互——解释了神经元活动中的大部分变异性。多个脑区和物种的神经元可以用定量模型来描述，这些模型捕捉了每个输入的测量依赖性，但对输入组合不做任何假设。这些最小模型等同于逻辑人工神经元，能够预测复杂的更高阶依赖性并恢复已知的突触连接特征。推断出的神经网络是稀疏的，表明其具有对扰动具有鲁棒性的高度冗余神经编码。这些结果表明，尽管存在复杂的生物物理细节，大多数神经元可以用简单的模型来描述。在神经元中，从输入到输出的映射涉及复杂的生物物理过程。尽管如此，现已证明简单的模型能够解释神经元活动变异性的大部分。

Keyword:

neural computation

neuronal activity

input-output dependencies

minimal models

synaptic connectivity

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Nature Physics

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yale university

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