Warning: Undefined array key "mm" in /www/wwwroot/www.ai-bt.com/si.php on line 10 Deprecated: trim(): Passing null to parameter #1 ($string) of type string is deprecated in /www/wwwroot/www.ai-bt.com/si.php on line 10 The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time.

Literature DB >> 508846

The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time.

Abstract

Mean and variance of the first passage time through a constant boundary for the Ornstein-Uhlenbeck process are determined by a straight-forward differentiation of the Laplace transform of the first passage time probability density function. The results of some numerical computations are discussed to shed some light on the input-output behavior of a formal neuron whose dynamics is modeled by a diffusion process of Ornstein-Uhlenbeck type.

Mesh：

Year: 1979 PMID： 508846 DOI： 10.1007/bf01845839

Source DB: PubMed Journal: Biol Cybern ISSN： 0340-1200 Impact factor: 2.086

4 in total

1. Diffusion approximation for a multi-input model neuron.

Authors: L M Ricciardi
Journal: Biol Cybern Date: 1976-11-30 Impact factor: 2.086

2. A model for neuron firing with exponential decay of potential resulting in diffusion equations for probability density.

Authors: B Gluss
Journal: Bull Math Biophys Date: 1967-06

3. Diffusion approximation and first passage time problem for a model neuron.

Authors: R M Capocelli; L M Ricciardi
Journal: Kybernetik Date: 1971-06

4. Analysis of the exponential decay model of the neuron showing frequency threshold effects.

Authors: B K Roy; D R Smith
Journal: Bull Math Biophys Date: 1969-06

4 in total

29 in total

1. The transient precision of integrate and fire neurons: effect of background activity and noise.

Authors: M C Van Rossum
Journal: J Comput Neurosci Date: 2001 May-Jun Impact factor: 1.621

2. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

Authors: Tiger W Lin; Anup Das; Giri P Krishnan; Maxim Bazhenov; Terrence J Sejnowski
Journal: Neural Comput Date: 2017-08-04 Impact factor: 2.026

3. Variable initial depolarization in Stein's neuronal model with synaptic reversal potentials.

Authors: P Lánský; M Musila
Journal: Biol Cybern Date: 1991 Impact factor: 2.086

4. Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data.

Authors: Paul Mullowney; Satish Iyengar
Journal: J Comput Neurosci Date: 2007-07-28 Impact factor: 1.621

5. Relationships between the threshold and slope of psychometric and neurometric functions during perceptual learning: implications for neuronal pooling.

Authors: Joshua I Gold; Chi-Tat Law; Patrick Connolly; Sharath Bennur
Journal: J Neurophysiol Date: 2009-10-28 Impact factor: 2.714

The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time.

1. Diffusion approximation for a multi-input model neuron.

2. A model for neuron firing with exponential decay of potential resulting in diffusion equations for probability density.

3. Diffusion approximation and first passage time problem for a model neuron.

4. Analysis of the exponential decay model of the neuron showing frequency threshold effects.

1. The transient precision of integrate and fire neurons: effect of background activity and noise.

2. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

3. Variable initial depolarization in Stein's neuronal model with synaptic reversal potentials.

4. Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data.

5. Relationships between the threshold and slope of psychometric and neurometric functions during perceptual learning: implications for neuronal pooling.

6. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

7. Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise.

8. Diffusion approximation of the neuronal model with synaptic reversal potentials.

9. Equilibrium and Response Properties of the Integrate-and-Fire Neuron in Discrete Time.

10. Instantaneous non-linear processing by pulse-coupled threshold units.