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

Zhaofei Yu, Shangqi Guo, Fei Deng, Qi Yan, Keke Huang, Jian K. Liu, Feng Chen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1347-1354
JournalIEEE Transactions on Cybernetics
Issue number3
Publication statusPublished - 2020


  • Biological neural networks
  • Brain modeling
  • Cybernetics
  • Hidden Markov models
  • Hidden Markov models (HMMs)
  • Markov processes
  • Mathematical model
  • neural implementation
  • Neurons
  • posterior inference
  • spiking neural network
  • winner-take-all (WTA) circuits

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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