Name
Title | ||
|---|---|---|
Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All. |
Abstract | ||
|---|---|---|
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 ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCYB.2018.2871144 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Hidden Markov models,Biological neural networks,Mathematical model,Neurons,Cybernetics,Markov processes,Brain modeling | Computational neuroscience,Inference,Posterior probability,Artificial intelligence,Hidden variable theory,Spiking neural network,Artificial neural network,Hidden Markov model,Winner-take-all,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
50 | 3 | 2168-2267 |
Citations | PageRank | References |
2 | 0.36 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaofei Yu | 1 | 38 | 16.83 |
| Shangqi Guo | 2 | 7 | 3.15 |
| Fei Deng | 3 | 19 | 6.59 |
| Qi Yan | 4 | 4 | 1.08 |
| Keke Huang | 5 | 41 | 10.22 |
| Jian K. Liu | 6 | 20 | 8.77 |
| Feng Chen | 7 | 431 | 33.92 |