Fast information transfer in neuronal systems rests on series of action potentials, the spike
trains, conducted along axons. Methods that compare spike trains are crucial for …

Parameters in diffusion neuronal models are divided into two groups; intrinsic and input
parameters. Intrinsic parameters are related to the properties of the neuronal membrane and …

We propose and discuss two information-based measures of statistical dispersion of positive
continuous random variables: the entropy-based dispersion and Fisher information-based …

Integrate and fire (IF) neurons have found widespread applications in computational
neuroscience. Particularly important are stochastic versions of these models where the …

We study a model of a stochastic process with unobserved parameters which suddenly
change at random times. The possible parameter values are assumed to be from a finite but …

During the stationary part of neuronal spiking response, the stimulus can be encoded in the
firing rate, but also in the statistical structure of the interspike intervals. We propose and …

LNCS 6927 - Neuronal Data Analysis Based on the Empirical Cumulative Entropy Page 1
Neuronal Data Analysis Based on the Empirical Cumulative Entropy Antonio Di Crescenzo1 …

We propose a novel measure of statistical dispersion of a positive continuous random
variable: the entropy-based dispersion (ED). We discuss the properties of ED and contrast …

The apparent stochastic nature of neuronal activity significantly affects the reliability of
neuronal coding. To quantify the encountered fluctuations, both in neural data and …

The time to the first spike after stimulus onset typically varies with the stimulation intensity.
Experimental evidence suggests that neural systems use such response latency to encode …