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Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All

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journal contribution
posted on 16.05.2019, 11:55 by Z Yu, S Guo, F Deng, Q Yan, K Huang, JK Liu, F Chen
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.

Funding

This work is supported in part by National Postdoctoral Program for Innovative Talents under Grant BX20180005, and in part by China Postdoctoral Science Foundation under Grant and 2018M630036 and 2017M620525, and in part by the National Natural Science Foundation of China under Grant 61671266, 61703439, 61327902, and in part by the Human Brain Project of the European Union #604102 and #720270.

History

Citation

IEEE Transactions on Cybernetics, 2018

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/Biological Sciences/Neuroscience, Psychology and Behaviour

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cybernetics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2168-2267

Copyright date

2018

Available date

16/05/2019

Publisher version

https://ieeexplore.ieee.org/document/8480115

Language

en