In the quest to model neuronal function amidst 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 the current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, act as controllers, steering their environment towards 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. Utilizing the novel Direct Data-Driven Control (DD-DC) framework, we model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states and optimize control. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) 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 novel and biologically-informed fundamental unit for constructing neural networks.

翻译：在生理数据缺失的情况下，为模拟神经元功能，一个颇有前景的策略是发展规范理论，将神经生理学解释为对计算目标的优化。本研究将当前主要优化预测的规范模型进行拓展，把神经元概念化为最优反馈控制器。我们主张，尤其是早期感觉区域之外的神经元，如同控制器一般，通过其输出将周围环境导向特定的期望状态。该环境既包含突触连接的神经元，也包含外部运动感觉反馈回路，使得神经元能够通过突触反馈评估其控制效能。利用新型直接数据驱动控制（DD-DC）框架，我们将神经元建模为具备生物可行性的控制器，该控制器能隐式识别回路动力学、推断潜在状态并优化控制。我们的DD-DC神经元模型解释了多种神经生理学现象：从长时程增强到长时程抑制在脉冲时序依赖可塑性（STDP）中伴随其非对称性的转变，前馈和反馈神经元滤波器的持续时间和自适应特性，恒定刺激下脉冲生成的不精确性，以及大脑中特有的操作变异性和噪声。我们的模型显著背离了传统的前馈、即时反应的McCulloch-Pitts-Rosenblatt神经元，为构建神经网络提供了一种新颖且基于生物学原理的基本单元。