First Passage Time Problem for the Ornstein-Uhlenbeck Neuronal Model

  • C. F. Lo
  • T. K. Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


In this paper we propose a simple and efficient method for computing accurate estimates (in closed form) of the first passage time density of the Ornstein-Uhlenbeck neuronal model through a fixed boundary (i.e. the interspike statistics of the stochastic leaky integrate-and-fire neuron model). This new approach can also provide very tight upper and lower bounds (in closed form) for the exact first passage time density in a systematic manner. Unlike previous approximate analytical attempts, this novel approximation scheme not only goes beyond the linear response and weak noise limit, but it can also be systematically improved to yield the exact results. Furthermore, it is straightforward to extend our approach to study the more general case of a deterministically modulated boundary.


Stochastic Resonance Barrier Option Multistage Approximation Regular Spike Cell Gauss Quadrature Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • C. F. Lo
    • 1
  • T. K. Chung
    • 1
  1. 1.Institute of Theoretical Physics and Department of PhysicsThe Chinese University of Hong KongShatin, N.T.Hong Kong

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