When are feedforward microcircuits well-modeled by maximum entropy methods?

When are microcircuits well-modeled by pairwise maximum entropy methods?

A Note on the Bivariate Maximum Entropy Modeling

Let X=(X1 ,X2 ) be a continuous random vector. Under the assumption that the marginal distributions of X1 and X2 are given, we develop models for vector X when there is partial information about the dependence structure between X1  and X2. The models which are obtained based on well-known Principle of Maximum Entropy are called the maximum entropy (ME) mo...

A Maximum Entropy Method for Particle Filtering

Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...

Genetic Feedforward-Feedback Controller for Functional Electrical Stimulation Control of Elbow Joint Angle

Background: Functional electrical stimulation (FES) is the most commonly used system for restoring functions after spinal cord injury (SCI). Objective: In this study we investigated feedback PID and feedforward-feedback P-PID controllers for regulating the elbow joint angle. Methods: The controllers were tuned based on a nonlinear muculoskeletal model containing two links, one joint with one de...

Determination of Maximum Bayesian Entropy Probability Distribution

In this paper, we consider the determination methods of maximum entropy multivariate distributions with given prior under the constraints, that the marginal distributions or the marginals and covariance matrix are prescribed. Next, some numerical solutions are considered for the cases of unavailable closed form of solutions. Finally, these methods are illustrated via some numerical examples.