Type III Responses to Transient Inputs in Hybrid Nonlinear Neuron Models

From Spiking Neuron Models to Linear-Nonlinear Models

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-line...

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...

Transient Responses in Dynamical Neural Models

We consider the input/output behavior of a realistic dynamical neural model in comparison to those typically used in artificial neural networks. We have found that such models duplicate well those behaviors seen in living neurons, displaying a range of behaviors commonly seen in a wide variety of nonlinear dynamical systems. This is not captured well by weighted sum/monotonic transfer function ...

Inference about the Burr Type III Distribution under Type-II Hybrid Censored Data

This paper presents the statistical inference on the parameters of the Burr type III distribution, when the data are Type-II hybrid censored. The maximum likelihood estimators are developed for the unknown parameters using the EM algorithm method. We provided the observed Fisher information matrix using the missing information principle which is useful for constructing the asymptotic confidence...

Predicting spike train responses of neuron models