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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Vydáno v:	IEEE transactions on cybernetics Ročník 49; číslo 1; s. 133 - 145
Hlavní autoři:	Guo, Shangqi, Yu, Zhaofei, Deng, Fei, Hu, Xiaolin, Chen, Feng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2168-2267 2168-2275 2168-2275
DOI:	10.1109/TCYB.2017.2768554