Spikes: exploring the neural code
Spikes: exploring the neural code
Stable propagation of activity pulses in populations of spiking neurons
Neural Computation
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks
Neural Computation
Optimal Signal Estimation in Neuronal Models
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
Stochastic dynamics of a finite-size spiking neural network
Neural Computation
Systematic fluctuation expansion for neural network activity equations
Neural Computation
First passage time problem for the ornstein-uhlenbeck neuronal model
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
First passage time problem for the ornstein-uhlenbeck neuronal model
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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We analyze the effect of noise in integrate-and-fire neurons driven by time-dependent input and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard function that has a gaussian dependence on the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.