Paper Info
Title
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits.
Abstract
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.
Year
Venue
Field
2018
arXiv: Neurons and Cognition
Joint probability distribution,Bayesian inference,Inference,Algorithm,Artificial intelligence,Probabilistic logic,Graphical model,Electronic circuit,Winner-take-all,Machine learning,Marginal distribution,Mathematics
DocType
Volume
Citations 
Journal
abs/1808.00675
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Zhaofei Yu13816.83
Yonghong Tian21057102.81
Tiejun Huang31281120.48
Jian K. Liu4208.77