Biomedical subjects

Enrico Rossoni

Publications and source records attributed to Enrico Rossoni.

4 recordsLinked to original sources

PubMed2006

Decoding spike train ensembles: tracking a moving stimulus.

We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a 'censored' maximum-likelihood estimator (CMLE) for decoding the input in an unbiased way when the spike activity is observed over time windows of finite length. Compared with a rate-based, moment estimator, the CMLE is proved consistently more efficient, particularly with nonstationary inputs. Using our approach, we show that a dynamical input to a group of neurons can be inferred accurately and with high temporal resolution (50 ms) using as few as about one spike per neuron within each decoding window. By applying our theoretical results to a population coding setting, we then demonstrate that a spiking neural network can encode spatial information in such a way to allow fast and precise tracking of a moving target.

Brain↗

PubMed2006

Dynamics of moment neuronal networks.

A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.

Action Potentials↗

PubMed2005

A nonparametric approach to extract information from interspike interval data.

In this work we develop an approach to extracting information from neural spike trains. Using the expectation-maximization (EM) algorithm, interspike interval data from experiments and simulations are fitted by mixtures of distributions, including Gamma, inverse Gaussian, log-normal, and the distribution of the interspike intervals of the leaky integrate-and-fire model. In terms of the Kolmogorov-Smirnov test for goodness-of-fit, our approach is proved successful (P>0.05) in fitting benchmark data for which a classical parametric approach has been shown to fail before. In addition, we present a novel method to fit mixture models to censored data, and discuss two examples of the application of such a method, which correspond to the case of multiple-trial and multielectrode array data. A MATLAB implementation of the algorithm is available for download from .

Action Potentials↗

PubMed2005

Stability of synchronous oscillations in a system of Hodgkin-Huxley neurons with delayed diffusive and pulsed coupling.

We study the synchronization dynamics for a system of two Hodgkin-Huxley (HH) neurons coupled diffusively or through pulselike interactions. By calculating the maximum transverse Lyapunov exponent, we found that, with diffusive coupling, there are three regions in the parameter space, corresponding to qualitatively distinct behaviors of the coupled dynamics. In particular, the two neurons can synchronize in two regions and desynchronize in the third. When excitatory and inhibitory pulse coupling is considered, we found that synchronized dynamics becomes more difficult to achieve in the sense that the parameter regions where the synchronous state is stable are smaller. Numerical simulations of the coupled system are presented to validate these results. The stability of a network of coupled HH neurons is then analyzed and the stability regions in the parameter space are exactly obtained.

Action Potentials↗