Nonrenewal spike trains generated by stochastic neuron models

Benjamin Lindner; Andre Longtin

doi:10.1117/12.488882

7 May 2003 Nonrenewal spike trains generated by stochastic neuron models

Benjamin Lindner, Andre Longtin

Author Affiliations +

Proceedings Volume 5114, Noise in Complex Systems and Stochastic Dynamics; (2003) https://doi.org/10.1117/12.488882
Event: SPIE's First International Symposium on Fluctuations and Noise, 2003, Santa Fe, New Mexico, United States

Abstract

Many of the stochastic neuron models employed in the neurobiological literature generate renewal point processes, i.e., successive intervals between spikes are statistically uncorrelated. Recently, however, much experimental evidence for positive and negative correlations in the interspike interval (ISI) sequence of real neurons has been accumulated. It has been shown that these correlations can have implications for neuronal functions. We study a leaky integrate-and-fire (LIF) model with a dynamical threshold or an adaptation current both of which lead to negative correlations. Conditions are identified where these models are equivalent. The ISI statistics, the serial correlation coefficient, and the power spectrum of the spike train, are numerically investigated for various parameter sets.

Citation Download Citation

Benjamin Lindner and Andre Longtin "Nonrenewal spike trains generated by stochastic neuron models", Proc. SPIE 5114, Noise in Complex Systems and Stochastic Dynamics, (7 May 2003); https://doi.org/10.1117/12.488882

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE