Hierarchical Bayesian Inference and Learning in Spiking Neural Networks

Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. Howev...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 49; no. 1; pp. 133 - 145
Main Authors: Guo, Shangqi, Yu, Zhaofei, Deng, Fei, Hu, Xiaolin, Chen, Feng
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bayes methods
Bayesian analysis
Biological neural networks
Biological system modeling
Brain
Computational modeling
Digits
Handwriting
Hidden Markov models
Hierarchical Bayesian model
Integrated circuit modeling
Machine learning
mean field theory
Neural networks
Neurons
spike-timing-dependent plasticity (STDP)
Spiking
spiking neural network
Statistical inference
variational expectation maximization
winner-takes-all (WTA) circuits
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. However, it remains unknown how such a computation is organized in the network of biologically plausible spiking neurons. In this paper, we propose a hierarchical network of winner-take-all circuits which can carry out hierarchical Bayesian inference and learning through a spike-based variational expectation maximization (EM) algorithm. Particularly, we show how the firing activities of spiking neurons in response to the input stimuli and the spike-timing-dependent plasticity rule can be understood, respectively, as variational E-step and M-step of variational EM. Finally, we demonstrate the utility of this spiking neural network on the MNIST benchmark for unsupervised classification of handwritten digits.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2768554