IRIS Uni Torinohttps://iris.unito.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Thu, 27 Jan 2022 00:20:07 GMT2022-01-27T00:20:07Z101051Statistical Techniques for Interferometric Signal Analysishttp://hdl.handle.net/2318/64321Titolo: Statistical Techniques for Interferometric Signal Analysis
Abstract: Phase disturbance due to atmospheric turbulence aects fringe tracking. Algorithms aimed at fringe parameter identification are based on interferometric models that have to be carefully adapted to the interfered beams, including sources of variability.
All the information is contained in the collected signals, and how to extract it is a major research problem.
This work aims to determine some stochastic properties of the signals from the data that are useful for modelling. We apply statistical techniques to real interferometric signals. We
examine the composition of signals before and after the combination using time series analysis, and we separate pure random eects and peculiar features from trends. Multivariate
regression analysis allows us to isolate noise components due to the interference physical process.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/2318/643212010-01-01T00:00:00ZInformation Measures in a Small Network of Spiking Neuronshttp://hdl.handle.net/2318/87753Titolo: Information Measures in a Small Network of Spiking Neurons
Abstract: We consider a small network, consisting of a reference unit that receives background inputs from the large network in which it is embedded and strong inputs from an excitatory unit and an inhibitory unit that generate constant amplitude jumps (upward or downward) in its membrane potential. The jumps occur at either exponential or inverse Gaussian distributed times. The entropy and the Kullback-Leibler distance between the spiking distributions are employed to study the effect of the inputs with a special attention to the role of inhibitory ones.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/2318/877532007-01-01T00:00:00ZDependencies between spike times of a couple of neurons modeled via a two-dimensional LIF modelhttp://hdl.handle.net/2318/81723Titolo: Dependencies between spike times of a couple of neurons modeled via a two-dimensional LIF model
Abstract: There exists a large literature on the Stein’s model. However, the largest part of these studies performs a diﬀusion limit on Stein’s equation to get a mathematically tractable stochastic process. Use of these continuous processes has allowed the discovery of various neuronal features
that are hidden in the original Stein’s model, for instance the stochastic resonance.
In this work, we consider a diﬀusion limit of two or more neuronal dynamics governed by Stein’s model to describe dependencies between their spike times. For this reason, we separate the PSPs impinging on each neuron into two groups, one with the PSPs coming from a common
network and the other one with those typical of the speciﬁc neuron.
We study the diﬀusion limit of these equations, superimposing a common threshold S and we describe the interspike intervals as ﬁrst passage times of the bidimensional diﬀusion processes through the boundary. The introduced dependency between the two Stein’s processes is maintained in the diﬀusion limit.
The aim of this work is to relate the introduced dependencies on the processes with those
obtained on the spike times of the two neurons, through their joint law.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/2318/817232010-01-01T00:00:00ZEffects of random jumps on a neuronal diffusion model with reversal potentialhttp://hdl.handle.net/2318/87752Titolo: Effects of random jumps on a neuronal diffusion model with reversal potential
Abstract: We consider a process obtained from the superposition of constant size jumps with exponentially distributed inter-events intervals to a diffusion process with state-dependent infinitesimal variance corresponding to a model neuron with intrinsic reversal potential. We study the main firing features of the model considered and we compare it with an analoguous one without reversal potential, to enlighten the role of the intrinsic lower boundary when inhibition dominates and when the jump size becomes relevant.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/2318/877522006-01-01T00:00:00ZEstimation of information measures in coupled diffusion neuronal modelshttp://hdl.handle.net/2318/92739Titolo: Estimation of information measures in coupled diffusion neuronal models
Abstract: Estimation of the mutual information between ISI's can be a useful tool to characterize dependencies in small neuronal networks. We introduce here a method to compute the mutual information of coupled model neurons by employing the copula function describing the dependence between the spiking distributions.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/2318/927392011-01-01T00:00:00Z(Leaky) integrate and fire models can be coincidence detectorshttp://hdl.handle.net/2318/127955Titolo: (Leaky) integrate and fire models can be coincidence detectors
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/2318/1279552012-01-01T00:00:00ZFirst entrance time distribution multimodality in a model neuronhttp://hdl.handle.net/2318/20237Titolo: First entrance time distribution multimodality in a model neuron
Abstract: A simple jump-diffusion neuronal model accounting for the spatial geometry of the cell is considered and the probability density function of the interspike interval distribution is studied to find out the conditions under which it becomes multimodal. An approximate formula for such density is obtained in the case where the underlying diffusion is a Wiener process with drift and the jumps are Poisson time distributed. This formula holds when the Poisson events are rare but correspond to jumps of relevant amplitude and it results to be valid in some neuronal modeling instance. Some examples are shown that illustrate its range of application.
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/2318/202372002-01-01T00:00:00ZThe first-passage-time problem with applications to neuronal modeling.http://hdl.handle.net/2318/108971Titolo: The first-passage-time problem with applications to neuronal modeling.
Tue, 01 Jan 1980 00:00:00 GMThttp://hdl.handle.net/2318/1089711980-01-01T00:00:00ZFirst passage time R packagehttp://hdl.handle.net/2318/127038Titolo: First passage time R package
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/2318/1270382012-01-01T00:00:00ZA simple estimator for mutual informationhttp://hdl.handle.net/2318/127039Titolo: A simple estimator for mutual information
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/2318/1270392012-01-01T00:00:00Z