Spikes: exploring the neural code
Spikes: exploring the neural code
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Firing rate of the noisy quadratic integrate-and-fire neuron
Neural Computation
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
Decoding spike train ensembles: tracking a moving stimulus
Biological Cybernetics
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process
Journal of Computational Neuroscience
Hi-index | 0.01 |
An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec.