Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 1, Pages 133-145Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2768554
Keywords
Hierarchical Bayesian model; mean field theory; spike-timing-dependent plasticity (STDP); spiking neural network; variational expectation maximization; winner-takes-all (WTA) circuits
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Funding
- National Natural Science Foundation of China [61671266, 61327902, 91420201]
- Research Project of Tsinghua University [20161080084]
- National High-Tech Research and Development Plan [2015AA042306]
- Beijing Municipal Science and Technology Commission [Z161100000216126]
- Huawei Technology [YB2015120018]
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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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