赢者通吃作为神经元回路的基本概率推理单位
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits
摘要：
神经科学的实验观察表明，大脑在计算不确定性信息时以概率的方式工作。这种处理可以被建模为贝叶斯推断。然而，目前还不清楚如何贝叶斯推断可以在大脑的神经元回路的水平上实施。在这项研究中，我们提出了一种新的通用神经实现的概率推理的基础上无处不在的网络皮层微电路，称为赢家通吃（WTA）电路。我们表明，每个WTA电路可以编码的分布状态定义在一个变量。通过将多个WTA电路连接在一起，可以表示任意概率图模型的联合分布。此外，我们证明了WTA电路的神经动力学能够实现概率图模型中最强大的推理方法之一，平均场推理。我们表明，在WTA电路中的每个尖峰神经元的突触驱动编码在每个状态的变量的边际概率，和每个神经元的发射概率（或发射率）是成比例的边际概率。理论分析和实验结果表明，WTA电路可以得到与平均场近似相当的推理结果。综上所述，我们的研究结果表明，WTA回路可以被视为神经元回路的最小推理单元。
英文摘要：
Experimental observations of neuroscience suggest that the brain is working a probabilistic way when computing information with uncertainty. This processing could be modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented at the level of neuronal circuits of the brain. In this study, we propose a novel general-purpose neural implementation of probabilistic inference based on a ubiquitous network of cortical microcircuits, termed winner-take-all (WTA) circuit. We show that each WTA circuit could encode the distribution of states defined on a variable. By connecting multiple WTA circuits together, the joint distribution can be represented for arbitrary probabilistic graphical models. Moreover, we prove that the neural dynamics of WTA circuit is able to implement one of the most powerful inference methods in probabilistic graphical models, mean-field inference. We show that the synaptic drive of each spiking neuron in the WTA circuit encodes the marginal probability of the variable in each state, and the firing probability (or firing rate) of each neuron is proportional to the marginal probability. Theoretical analysis and experimental results demonstrate that the WTA circuits can get comparable inference result as mean-field approximation. Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits.
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