In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch–Pitts–Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

中文翻译：

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神经元作为直接数据驱动控制器


为了在生理数据的空白中模拟神经元功能，一个有前途的策略是开发一种规范理论，将神经元生理学解释为优化计算目标。这项研究通过将神经元概念化为最佳反馈控制器来扩展当前的规范模型，该模型主要优化预测。我们假设神经元，尤其是早期感觉区域之外的神经元，通过其输出将其环境引导至特定的所需状态。该环境包括突触互连的神经元和外部运动感觉反馈回路，使神经元能够通过突触反馈评估其控制的有效性。为了将神经元建模为生物学上可行的控制器，隐式识别循环动态、推断潜在状态并优化控制，我们利用当代直接数据驱动控制（DD-DC）框架。我们的 DD-DC 神经元模型解释了各种神经生理现象：尖峰时序依赖性可塑性从增强到抑制的转变及其不对称性、前馈和反馈神经元滤波器的持续时间和适应性、持续刺激下尖峰生成的不精确性，以及大脑中特有的操作变异性和噪音。我们的模型与传统的前馈、即时响应的 McCulloch-Pitts-Rosenblatt 神经元有很大不同，为构建神经网络提供了现代的、基于生物学的基本单元。