We demonstrate a self-organizing system based on photorefractive ringoscillators. We employ the system in two ways that can both be thought of as feature extractors; one acts on a set of images exposed repeatedly to the system strictly as a linear feature extractor, and the other serves as a signal demultiplexer forfiber optic communications. Both systems implement unsupervised competitive learning embedded within the mode interaction dynamics between the modes of a set of ring oscillators. Aftera training period, the modes of the rings become associated withthe different image features or carrier frequencies within the incoming data stream.

Stochastic gradient descent is a general algorithm which includes LMS, online backpropagation, and adaptive k-means clustering as special cases.

Joshua Alspector Anthony Jayakumar Stephan Lunat Bellcore Morristown, NJ 07962-1910 Abstract We report learning measurements from a system composed of a cascadable learning chip, data generators and analyzers for training pattern presentation, and an X-windows based software interface. The 32 neuron learning chip has 496 adaptive synapses and can perform Boltzmann and mean-field learning using separate noise and gain controls.

THE MODEL; STOCHASTIC RESONANCE The reduced neuron model consists of a single Hopfield-type computational element, which may be modeled as a R-C circuit with nonlinear feedback provided by an operational amplifier having a sigmoid transfer function.

The subject of this paper is the integration of multi-layered Artificial Neural Networks(ANN) with probability density functions such as Gaussian mixtures found in continuous density Hidden Markov Models (HMM). In the first part of this paper we present an ANN/HMM hybrid in which all the parameters of the the system are simultaneously optimized with respect to a single criterion. In the second part of this paper, we study the relationship between the density of the inputs of the network and the density of the outputs of the networks. A few experiments are presented to explore how to perform density estimation with ANNs. 1 INTRODUCTION This paper studies the integration of Artificial Neural Networks (ANN) with probability densityfunctions (pdf) such as the Gaussian mixtures often used in continuous density Hidden Markov Models. The ANNs considered here are multi-layered or recurrent networks with hyperbolic tangent hidden units.

Because eye muscles never cocontract and do not deal with external loads, one can write an equation that relates motoneuron firing rate to eye position and velocity - a very uncommon situation in the CNS. The semicircular canals transduce head velocity in a linear manner by using a high background discharge rate, imparting linearity to the premotor circuits that generate eye movements. This has allowed deducing some of the signal processing involved, including a neural network that integrates. These ideas are often summarized by block diagrams. Unfortunately, they are of little value in describing the behavior of single neurons - a fmding supported by neural network models.

A neurophysiologically-based model is presented that controls a simulated kinematic arm during goal-directed reaches. The network generates a quasi-feedforward motor command that is learned using training signals generated by corrective movements. For each target, the network selects and sets the output of a subset of pattern generators. During the movement, feedbackfrom proprioceptors turns off the pattern generators. The task facing individual pattern generators is to recognize when the arm reaches the target and to turn off. A distributed representation of the motor commandthat resembles population vectors seen in vivo was produced naturally by these simulations.

We define the concept of polynomial uniform convergence of relative frequencies to probabilities in the distribution-dependent context.

We present a Parallel Distributed Semantic (PDS) Network architecture that addresses the problems of sequencing and ambiguity resolution in natural language understanding. A PDS Network stores phrases and their meanings using multiple PDP networks, structured in the form of a semantic net.A mechanism called Propagation Filters is employed: (1) to control communication between networks, (2) to properly sequence the components of a phrase, and (3) to resolve ambiguities. Simulation results indicate that PDS Networks and Propagation Filters can successfully represent high-levelknowledge, can be trained relatively quickly, and provide for parallel inferencing at the knowledge level. 1 INTRODUCTION Backpropagation has shown considerable potential for addressing problems in natural languageprocessing (NLP). However, the traditional PDP [Rumelhart and McClelland, 1986] approach of using one (or a small number) of backprop networks for NLP has been plagued by a number of problems: (1) it has been largely unsuccessful atrepresenting high-level knowledge, (2) the networks are slow to train, and (3) they are sequential at the knowledge level. A solution to these problems is to represent high-level knowledge structures over a large number of smaller PDP net-233 234 Sumida and Dyer works.

We describe a neural network, called RufeNet, that learns explicit, symbolic condition-action rules in a formal string manipulation domain. of the domain,RuleNet discovers functional categories over elements and, at various points during learning, extracts rules that operate on these categories. The rules are then injected back into RuleNet and in a process called iterative projection. By incorporatingtraining continues, rules in this way, RuleNet exhibits enhanced learning and generalization performance over alternative neural net approaches. By integrating symbolic rule learning and subsymbolic category learning, RuleNet has capabilities that go beyond a purely symbolic system. We show how this architecture can be applied to the problem of case-role assignment in natural language processing, yielding a novel rule-based solution.