We present a unified framework for a number of different ways of failing to generalize properly.

We describe a recurrent connectionist network, called CONCERT, that uses a set of melodies written in a given style to compose new melodies in that style. CONCERT is an extension of a traditional algorithmic composition technique inwhich transition tables specify the probability of the next note as a function of previous context. A central ingredient of CONCERT is the use of a psychologically-grounded representation of pitch.

We present a large vocabulary, continuous speech recognition system based on Linked Predictive Neural Networks (LPNN's). The system uses neural networksas predictors of speech frames, yielding distortion measures which are used by the One Stage DTW algorithm to perform continuous speech recognition. The system, already deployed in a Speech to Speech Translation system, currently achieves 95%, 58%, and 39% word accuracy on tasks with perplexity 5, 111, and 402 respectively, outperforming several simpleHMMs that we tested. We also found that the accuracy and speed of the LPNN can be slightly improved by the judicious use of hidden control inputs. We conclude by discussing the strengths and weaknesses of the predictive approach.

The main point of this paper is that stochastic neural networks have a mathematical structure that corresponds quite closely with that of quantum field theory. Neural network Liouvillians and Lagrangians can be derived, just as can spin Hamiltonians and Lagrangians in QFf. It remains to show the efficacy of such a description.

A recurrent back-propagation neural network architecture was then designed to numerically estimate the parameters of an optimal nonlinear control law for landing theaircraft. The performance of the network was then evaluated.

K. Schulten Beckman-Insti t ute University of Illinois Urbana, IL 61801 Feature selective cells in the primary visual cortex of several species are organized inhierarchical topographic maps of stimulus features like "position in visual space", "orientation" and" ocular dominance". In order to understand anddescribe their spatial structure and their development, we investigate aself-organizing neural network model based on the feature map algorithm. The model explains map formation as a dimension-reducing mapping from a high-dimensional feature space onto a two-dimensional lattice, such that "similarity" between features (or feature combinations) is translated into "spatial proximity" between the corresponding feature selective cells. The model is able to reproduce several aspects of the spatial structure of cortical maps in the visual cortex. 1 Introduction Cortical maps are functionally defined structures of the cortex, which are characterized byan ordered spatial distribution of functionally specialized cells along the cortical surface. In the primary visual area(s) the response properties of these cells must be described by several independent features, and there is a strong tendency to map combinations of these features onto the cortical surface in a way that translates "similarity" into "spatial proximity" of the corresponding feature selective cells (see e.g.

Its architecture is based on wellestablished psychologicaltheory, and is related to networks using radial basis functions.

F. Fallside We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections.This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L

Holmdel, NJ 07733, USA The learning time of a simple neural network model is obtained through an analytic computation of the eigenvalue spectrum for the Hessian matrix, which describes the second order properties of the cost function in the space of coupling coefficients. The form of the eigenvalue distribution suggests new techniques for accelerating the learning process, and provides a theoretical justification for the choice of centered versus biased state variables.

A network based on splines is described.