A bi-stable neuronal model of Gibbs distribution
In this paper we present a bi-stable neuronal model consistent with the Gibbs distribution. Our approach utilizes a formalism used in stochastic (Boltzmann) machines with a bistable-neuron algorithm in which each neuron can exist in either an ON or an OFF state. The transition between the system’s s...
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| Published in | Physica A Vol. 429; pp. 118 - 124 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.07.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0378-4371 1873-2119 |
| DOI | 10.1016/j.physa.2015.02.066 |
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| Abstract | In this paper we present a bi-stable neuronal model consistent with the Gibbs distribution. Our approach utilizes a formalism used in stochastic (Boltzmann) machines with a bistable-neuron algorithm in which each neuron can exist in either an ON or an OFF state. The transition between the system’s states is composed of two random processes, the first one decides which state transition should be attempted and the second one decides if the transition is accepted or not. Our model can be easily extended to systems with asymmetrical weight matrices.
•A bi-stable neuron model is presented from which the Gibbs distribution can be derived.•The model can be implemented in a stochastic (Boltzmann) machine composed of a system of bi-stable neurons.•The model may have important implications in neural networks and Markov logic networks. |
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| AbstractList | In this paper we present a bi-stable neuronal model consistent with the Gibbs distribution. Our approach utilizes a formalism used in stochastic (Boltzmann) machines with a bistable-neuron algorithm in which each neuron can exist in either an ON or an OFF state. The transition between the system’s states is composed of two random processes, the first one decides which state transition should be attempted and the second one decides if the transition is accepted or not. Our model can be easily extended to systems with asymmetrical weight matrices.
•A bi-stable neuron model is presented from which the Gibbs distribution can be derived.•The model can be implemented in a stochastic (Boltzmann) machine composed of a system of bi-stable neurons.•The model may have important implications in neural networks and Markov logic networks. |
| Author | Gross, Eitan |
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| Cites_doi | 10.2307/1426035 10.1111/j.2517-6161.1979.tb01052.x 10.1137/1113026 10.1093/bioinformatics/btg1026 10.1112/blms/5.1.81 10.1126/science.1165821 10.1063/1.1699114 10.1007/BF02761538 10.1073/pnas.79.8.2554 10.1209/0295-5075/100/58004 10.1071/PH650119 10.1089/1066527041410346 10.1214/07--AOAS145 10.2307/2317621 10.1207/s15516709cog0901_7 10.1111/j.2517-6161.1974.tb00999.x |
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| Title | A bi-stable neuronal model of Gibbs distribution |
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