Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-20T01:01:54.609Z Has data issue: false hasContentIssue false

Use of an hourglass model in neuronal coding

Published online by Cambridge University Press:  14 July 2016

M. Cottrell*
Affiliation:
Université Paris 1
T. S. Turova*
Affiliation:
University of Lund
*
Postal address: SAMOS, Université Paris 1, 90 rue de Tolbiac, F-75634 Paris Cedex 13, France
∗∗Postal address: Department of Mathematical Statistics, University of Lund, Box 118, S-221 00 Lund, Sweden. Email address: [email protected]

Abstract

We study a system of interacting renewal processes which is a model for neuronal activity. We show that the system possesses an exponentially large number (with respect to the number of neurons in the network) of limiting configurations of the ‘firing neurons’. These we call patterns. Furthermore, under certain conditions of symmetry we find an algorithm to control limiting patterns by means of the connection parameters.

Type
Research Papers
Copyright
Copyright © 2000 by The Applied Probability Trust 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bovier, A. and Picco, P. (eds). Mathematical Aspects of Spin Glasses and Neural Networks. Birkhäuser, Basel, 1998.CrossRefGoogle Scholar
Cottrell, M. (1992). Mathematical analysis of a neural network with inhibitory coupling. Stoch. Proc. Appl. 40, 103126.CrossRefGoogle Scholar
Cottrell, M., Piat, F., and Rospars, J.-P. (1997). A stochastic model for interconnected neurons. Biosystems 40, 2935.CrossRefGoogle ScholarPubMed
Davis, M. H. A. (1993). Markov Models and Optimization. Chapman and Hall, New York.Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 25542558.Google Scholar
Karpelevich, F., Malyshev, V. A., and Rybko, A. N. (1995). Stochastic evolution of neural networks. Markov Proc. Rel. Fields 1, 141161.Google Scholar
Malyshev, V. A., and Turova, T. S. (1997). Gibbs measures on attractors in biological neural networks. Markov Proc. Rel. Fields 3, 443464.Google Scholar
Piat, F. (1994). Modélisation du système olfactif par une approche neuromimétique: aspects cognitifs et théoriques. Ph.D. thesis, Université Paris 1.Google Scholar
Stone, C. (1965). On moment generating functions and renewal theory. Ann. Math. Statist. 36, 12981301.CrossRefGoogle Scholar
Turova, T .S., (1996). Analysis of a biologically plausible neural network via an hourglass model. Markov Proc. Rel. Fields 2, 487510.Google Scholar
Turova, T. S. (1998). Exponential rate of convergence of an infinite neuron model with local connections. Stoch. Proc. Appl. 73, 173193.CrossRefGoogle Scholar