Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44118
Title: Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All
Authors: Yu, Z
Guo, S
Deng, F
Yan, Q
Huang, K
Liu, JK
Chen, F
First Published: 3-Oct-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Cybernetics, 2018
Abstract: IEEE Hidden Markov models (HMMs) underpin the solution to many problems in computational neuroscience. However, it is still unclear how to implement inference of HMMs with a network of neurons in the brain. The existing methods suffer from the problem of being nonspiking and inaccurate. Here, we build a precise equivalence between the inference equation of HMMs with time-invariant hidden variables and the dynamics of spiking winner-take-all (WTA) neural networks. We show that the membrane potential of each spiking neuron in the WTA circuit encodes the logarithm of the posterior probability of the hidden variable in each state, and the firing rate of each neuron is proportional to the posterior probability of the HMMs. We prove that the time course of the neural firing rate can implement posterior inference of HMMs. Theoretical analysis and experimental results show that the proposed WTA circuit can get accurate inference results of HMMs.
DOI Link: 10.1109/TCYB.2018.2871144
ISSN: 2168-2267
Links: https://ieeexplore.ieee.org/document/8480115
http://hdl.handle.net/2381/44118
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Institute of Electrical and Electronics Engineers (IEEE). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Neuroscience, Psychology and Behaviour

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