Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival.

Reduced models of neuronal activity such as integrate-and-fire models allow a description of neuronal dynamics in simple, intuitive terms and are easy to simulate numerically. We present a method to fit an integrate-and-fire-type model of neuronal activity, namely a modified version of the spike response model, to a detailed Hodgkin-Huxley-type neuron model driven by stochastic spike arrival. I...

Predicting spike times from subthreshold dynamics of a neuron

It has been established that a neuron reproduces highly precise spike response to identical fluctuating input currents. We wish to accurately predict the firing times of a given neuron for any input current. For this purpose we adopt a model that mimics the dynamics of the membrane potential, and then take a cue from its dynamics for predicting the spike occurrence for a novel input current. It...

Predicting Spike Train Responses of Neuron Models Predicting Spike Train Responses of Neuron Models

It is shown how neural spike train responses can be predicted by truncated Wiener series and by LN-cascade models. To prove the capability of these methods we test them on spike trains which have been generated by model neurons. The agreement of the approximated responses and the neuron response to known stimuli is analysed quantitatively by calculating least mean square errors and rates of coi...

Predicting spike-timing of a thalamic neuron using a stochastic synaptic model

A twofold spike-timing dependent stochastic synaptic model is used along with a leaky Integrate-and-Fire neuronal model to predict the spike timing of a single post-synaptic neuron in the lateral geniculate nucleus, knowing the spike train on the pre-synaptic side (i.e. in a retinal ganglion cell). In this synaptic model, spike-timing dependency is introduced for both the magnitude and relaxati...

Predicting spike train responses of neuron models