A bi-stable neuronal model of Gibbs distribution

Eitan Gross

Physica A: Statistical Mechanics and its Applications, 2015, vol. 429, issue C, 118-124

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.

Keywords: Boltzmann machines; Gibbs random field; Markov chain (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437115001831
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:429:y:2015:i:c:p:118-124

DOI: 10.1016/j.physa.2015.02.066

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().