Neuroscience Institute, NYU Langone Medical School（纽约大学朗格尼医学院神经科学研究所）；Center for Computational Neuroscience, Flatiron Institute（熨斗研究所计算神经科学中心）；Physics Department, Indiana University（印第安纳大学物理系）
Jason Moore, Alexander Genkin, Magnus Tournoy, Joshua Pughe-Sanford, Rob R. de Ruyter van Steveninck, Dmitri B. Chklovskii
本研究将神经元概念化为最优反馈控制器，利用 Direct Data-Driven Control (DD-DC) 框架建模其隐式识别动态、推断状态并优化控制的过程，解释了多种神经生理现象，为构建神经网络提供了新颖的生物学基本单元。
摘要：
为了在生理数据存在空白的情况下对神经元功能进行建模，一种有前景的策略是发展一种规范性理论，将神经元生理学解释为对计算目标的优化。本研究扩展了当前主要优化预测的规范性模型，将神经元概念化为最优反馈控制器。我们假设神经元，尤其是早期感觉区域之外的神经元，充当控制器的角色，通过其输出将环境引导至特定的期望状态。该环境包含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。利用新颖的 Direct Data-Driven Control (DD-DC) 框架，我们将神经元建模为生物学上可行的控制器，该控制器能够隐式识别回路动态、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现象：Spike-Timing-Dependent Plasticity (STDP) 中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与自适应性、在恒定刺激下产生脉冲的不精确性，以及大脑特有的操作变异性和噪声。我们的模型与传统的、前馈的、即时响应的 McCulloch-Pitts-Rosenblatt 神经元显著不同，为构建神经网络提供了一个新颖且具有生物学依据的基本单元。
英文摘要：
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.
INTENSE: Information-theoretic discovery of behavioral selectivity of neurons in calcium imaging data
Institute for Advanced Brain Studies of the Moscow State University（莫斯科国立大学高级脑科学研究所）；Center for Interdisciplinary Research in Biology, Collège de France（法兰西公学院跨学科生物学研究中心）；Semenov Institute of Chemical Physics, Russian Academy of Sciences（俄罗斯科学院谢苗诺夫化学物理研究所）
How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity
Massachusetts Institute of Technology（麻省理工学院）；NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI)（美国国家科学基金会人工智能基础相互作用AI研究所）
