Name
Abstract | ||
|---|---|---|
We propose an asynchronous neural network model having the same structure as the binary Hopfield model. Each neuron operates with continuous time and randomly changes its state according only to its membrane potential. The proposed model settles in a steady-state fluctuation, in which the probability distribution of the global state is identical to that of the serial Boltzmann machine with the same synaptic weights. © 1997 Elsevier Science Ltd. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1016/S0893-6080(97)00006-3 | Neural Networks |
Keywords | Field | DocType |
boltzmann machine,learning,hopfield model,neural networks,asynchronous dynamics,associative memory,continuous-time asynchronous boltzmann machine,probability distribution,membrane potential,neural network model,steady state,neural network | Asynchronous communication,Boltzmann equation,Boltzmann machine,Content-addressable memory,Computer science,Probability distribution,Artificial intelligence,Artificial neural network,Machine learning,Binary number | Journal |
Volume | Issue | ISSN |
10 | 6 | Neural Networks |
Citations | PageRank | References |
3 | 0.86 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuo Yamanaka | 1 | 16 | 5.69 |
| Masahiro Agu | 2 | 4 | 2.92 |
| Teruyuki Miyajima | 3 | 59 | 13.46 |