Switching Time Statistics for Driven Neuron Models: Analytic Expressions versus Numerics

نویسندگان

  • Michael Schindler
  • Peter Talkner
  • Peter Hanggi
چکیده

Analytical expressions are put forward to investigate the forced spiking activity of abstract neuron models such as the driven leaky integrate-and-fire model. The method is valid in a wide parameter regime beyond the restraining limits of weak driving (linear response) and/or weak noise. The novel approximation is based on a discrete state Markovian modeling of the full long-time dynamics with time-dependent rates. The scheme yields excellent agreement with numerical Langevin and FokkerPlanck simulations of the full non-stationary dynamics, not only for the first-passage time statistics, but also for the important interspike interval (residence time) distribution.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Firing time statistics for driven neuron models: analytic expressions versus numerics.

Analytical expressions are put forward to investigate the forced spiking activity of abstract neuron models such as the driven leaky integrate-and-fire model. The method is valid in a wide parameter regime beyond the restraining limits of weak driving (linear response) and/or weak noise. The novel approximation is based on a discrete state Markovian modeling of the full long-time dynamics with ...

متن کامل

Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies

Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical integration approaches because the timing of spikes is treated exactly. The dr...

متن کامل

Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise

We study the one-dimensional normal form of a saddle-node system under the influence of additive gaussian white noise and a static "bias current" input parameter, a model that can be looked upon as the simplest version of a type I neuron with stochastic input. This is in contrast with the numerous studies devoted to the noise-driven leaky integrate-and-fire neuron. We focus on the firing rate a...

متن کامل

Effects of Viscosity Variations on Buoyancy-Driven Flow from a Horizontal Circular Cylinder Immersed in Al2O3-Water Nanofluid

The buoyancy-driven boundary-layer flow from a heated horizontal circular cylinder immersed in a water-based alumina (Al2O3) nanofluid is investigated using variable properties for nanofluid viscosity. Two different viscosity models are utilized to evaluate heat transfer enhancement from a cylinder. Exact analytic solutions of the problem are attained employing a novel...

متن کامل

Integrate-and-Fire Neurons Driven by Correlated Stochastic Input

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that repres...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2004