Laura Sacerdote

On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity

Diffusion processes have been extensively used to describe membrane potential behavior. In this approach the interspike interval has a theoretical counterpart in the first-passage-time of the diffusion model employed. Since the mathematical complexity of the first-passage-time problem increases with attempts to make the models more realistic it seems useful to compare the features of different models in order to highlight their relative performance. In this paper we compare the Feller and Ornstein-Uhlenbeck models under three different criteria derived from the level of information available about their parameters. We conclude that the Feller model is preferable when complete knowledge of the characterizing parameters is assumed. On the other hand, when only limited information about the parameters is available, such as the mean firing time and the histogram shape, no advantage arises from using this more complex model.

The Ornstein–Uhlenbeck neuronal model with signal-dependent noise

Abstract The Ornstein–Uhlenbeck neuronal model is investigated under the assumption that the amplitude of the noise is signal dependent. A linear approximation of the input–output transfer function is developed. Different types of dependencies of the noise on the signal are considered. The conditions are presented under which a substantial difference between transfer functions with constant and signal-dependent noise appears. An example for which the transfer function yields contra-intuitive behavior is presented.

Mean instantaneous firing frequency is always higher than the firing rate

Frequency coding is considered one of the most common coding strategies employed by neural systems. This fact leads, in experiments as well as in theoretical studies, to construction of so-called transfer functions, where the output firing frequency is plotted against the input intensity. The term firing frequency can be understood differently in different contexts. Basically, it means that the number of spikes over an interval of preselected length is counted and then divided by the length of the interval, but due to the obvious limitations, the length of observation cannot be arbitrarily long. Then firing frequency is defined as reciprocal to the mean interspike interval. In parallel, an instantaneous firing frequency can be defined as reciprocal to the length of current interspike interval, and by taking a mean of these, the definition can be extended to introduce the mean instantaneous firing frequency. All of these definitions of firing frequency are compared in an effort to contribute to a better understanding of the input-output properties of a neuron.

A Monte Carlo Method for the Simulation of First Passage Times of Diffusion Processes

A reliable Monte Carlo method for the evaluation of first passage times of diffusion processes through boundaries is proposed. A nested algorithm that simulates the first passage time of a suitable tied-down process is introduced to account for undetected crossings that may occur inside each discretization interval of the stochastic differential equation associated to the diffusion. A detailed analysis of the performances of the algorithm is then carried on both via analytical proofs and by means of some numerical examples. The advantages of the new method with respect to a previously proposed numerical-simulative method for the evaluation of first passage times are discussed. Analytical results on the distribution of tied-down diffusion processes are proved in order to provide a theoretical justification of the Monte Carlo method.

An improved technique for the simulation of first passage times for diffusion processes

Improved simulation schemes for the evaluation of first passage times through boundaries for one dimensional diffusion processes can be obtained taking into account possible crossings that occur inside each simulation interval. Approximated evaluations for the probabilities of such events are proposed and the error connected with their use in a simulation algorithm is discussed. Two examples are finally given to illustrate the features of the method.

Stochastic Integrate and Fire Models: a review on mathematical methods and their applications

Mathematical models are an important tool for neuroscientists. During the last thirty years many papers have appeared on single neuron description and specifically on stochastic Integrate and Fire models. Analytical results have been proved and numerical and simulation methods have been developed for their study. Reviews appeared recently collect the main features of these models but do not focus on the methodologies employed to obtain them. Aim of this paper is to fill this gap by upgrading old reviews on this topic. The idea is to collect the existing methods and the available analytical results for the most common one dimensional stochastic Integrate and Fire models to make them available for studies on networks. An effort to unify the mathematical notations is also made. This review is divided in two parts: Derivation of the models with the list of the available closed forms expressions for their characterization; Presentation of the existing mathematical and statistical methods for the study of these models.

On the Probability Densities of an Ornstein-Uhlenbeck Process with a Reflecting Boundary

We show that the transition p.d.f. of the Ornstein-Uhlenbeck process with a reflection condition at an assigned state S is related by integral-type equations to the free transition p.d.f., to the transition p.d.f. in the presence of an absorption condition at S, to the first-passage-time p.d.f. to S and to the probability current. Such equations, that are seen to be useful also for computational purposes, yield as an immediate consequence all known closed form results for Ornstein-Uhlenbeck process.

On the inverse first-passage-time problem for a Wiener process

The inverse first-passage problem for a Wiener process

Jump-diffusion processes as models for neuronal activity☆

(W_t)_{t\ge0}

A first passage problem for a bivariate diffusion process: Numerical solution with an application to neuroscience when the process is Gauss-Markov

seeks to determine a function