A hierarchical Bayesian model for inferring neural tuning functions from voxel tuning functions

Tuning Evaluation Functions for Search

Fine Tuning of Membership Functions for Fuzzy Neural Systems

This paper presents a new method for fine-tuning the Gaussian membership functions of a fuzzy neural network (FNN) to improve approximation accuracy. This method results in special shape membership functions without the convex property. We first recall that any continuous function can be represented by a linear combination of Gaussian functions with any standard deviation. Therefore, the Gaussi...

Tuning Vector Parametrized Reinforcement Functions

Juan M. Santos [email protected] Andreas Matt [email protected] Claude F. Touzet [email protected] Depto.de Computación, FCEN; University of Buenos Aires; Cdad.Universitaria, Pab.I; Buenos Aires, Argentina Institute of Mathematics, University of Innsbruk, Viktor-Franz-Hess Haus, Technikerstr.25/7, A-6020 Innsbruck, Austria Laboratoire de Neurobiologie Humaine UMR 6265 ; Universi...

Neural Tuning Functions Underlie Both Generalization and Interference

In sports, the role of backswing is considered critical for generating a good shot, even though it plays no direct role in hitting the ball. We recently demonstrated the scientific basis of this phenomenon by showing that immediate past movement affects the learning and recall of motor memories. This effect occurred regardless of whether the past contextual movement was performed actively, pass...

Radial Basis Functions Neural Network Based Self-Tuning Regulator

In this paper a new technique is proposed to design an online control algorithm using the Radial Basis Functions Neural Network (RBFNN). The controller is an RBFNN based direct self-tuning regulator (STR) that overcomes several shortcomings of the inverse control design using the neural networks. The control algorithm performs equally good to both minimum phase and non-minimum phase plants. The...