Abstract
In the paper a diffusion model of a neuron is treated. A new, less restrictive than usually, condition of applicability of a diffusion model is presented. As a result the point-process-to-point-process model of a neuron is obtained, which produces an output signal of the same kind as the accepted input signals.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Grigelionis B (1963) On the convergence of sums of random step process to a Poisson process. Teor Verojatn Prim 8:189–194
Holden AV (1976) Models of the stochastic activity of neurons. In: Lecture Notes in Biomathematics, vol 12. Springer, Berlin Heidelberg New York
Karlin S (1975) First course in stochastic processes. Academic Press, New York
Lansky P (1984) On approximations of Stein's neuronal model. J Theor Biol 107:631–647
Pacut A, Dąbrowski L (1988) Delayed-exponential approximation of a linear homogeneous diffusion model of neuron. Biol Cybern 59:395–404
Ricciardi LM (1976) Diffusion approximation for a multi-input model neuron. Biol Cybern 24:237–240
Sampath G, Srinivasan SK (1977) Stochastic models for spike trains of single neurons. In: Lecture Notes in Biomathematics, vol 16. Springer, Berlin Heidelberg New York
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Dąbrowski, L. A diffusion model of a neuron and neural nets. Biol. Cybern. 68, 451–454 (1993). https://doi.org/10.1007/BF00198777
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00198777