The effects of parameter modifications imposed by hardware constraints on a self-organizing feature map algorithm were examined. Performance was measured by the error rate of a speech recognition system which included this algorithm as part of the front-end processing. System parameters which were varied included weight (connection strength) quantization, adap tation quantization, distance measures and circuit approximations which include device characteristics and process variability. Experiments using the TI isolated word database for 16 speakers demonstrated degradation in performance when weight quantization fell below 8 bits. The competitive nature of the algorithm rela..xes constraints on uniformity and linearity which makes it an excellent candidate for a fully analog circuit implementation. Prototype circuits have been fabricated and characterized following the constraints established through the simulation efforts. 1 Introduction The self-organizing feature map algorithm developed by Kohonen [Kohonen, 1988] readily lends itself to the task of vector quantization for use in such areas as speech recognition.

It is generally admitted that generalization performance of back-propagation networks (Rumelhart, Hinton & Williams, 1986) will depend on the relative size ofthe training data and of the trained network. By analogy to curve-fitting and for theoretical considerations, the generalization performance of the network should decrease as the size of the network and the associated number of degrees of freedom increase (Rumelhart, 1987; Denker et al., 1987; Hanson & Pratt, 1989). This paper examines the dynamics of the standard back-propagation algorithm (BP) and of a constrained back-propagation variation (CBP), designed to adapt the size of the network to the training data base. The performance, learning dynamics and the representations resulting from the two algorithms are compared.

The mapping of the back-propagation and mean field theory learning algorithms onto a generic 2-D SIMD computer is described. This architecture proves to be very adequate for these applications since efficiencies close to the optimum can be attained. Expressions to find the learning rates are given and then particularized to the DAP array procesor.

We study networks of spiking neurons in which spikes are fired as a Poisson process. The state of a cell is determined by the instantaneous firing rate, and in the limit of high firing rates our model reduces to that studied by Hopfield. We find that the inclusion of spiking results in several new features, such as a noise-induced asymmetry between "on" and "off" states of the cells and probability currents which destroy the usual description of network dynamics in terms of energy surfaces. Taking account of spikes also allows us to calibrate network parameters such as "synaptic weights" against experiments on real synapses. Realistic forms of the post synaptic response alters the network dynamics, which suggests a novel dynamical learning mechanism.

Spiking neurons which integrate to threshold and fire were used to study the transmission of frequency modulated (FM) signals through layered networks. Firing correlations between cells in the input layer were found to modulate the transmission of FM signals under certain dynamical conditions. A tonic level of activity was maintained by providing each cell with a source of Poissondistributed synaptic input. When the average membrane depolarization produced by the synaptic input was sufficiently below threshold, the firing correlations between cells in the input layer could greatly amplify the signal present in subsequent layers. When the depolarization was sufficiently close to threshold, however, the firing synchrony between cells in the initial layers could no longer effect the propagation of FM signals. In this latter case, integrateand-fire neurons could be effectively modeled by simpler analog elements governed by a linear input-output relation.

The effects of parameter modifications imposed by hardware constraints on a self-organizing feature map algorithm were examined. Performance was measured by the error rate of a speech recognition system which included this algorithm as part of the front-end processing. System parameters which were varied included weight (connection strength) quantization, adap tation quantization, distance measures and circuit approximations which include device characteristics and process variability. Experiments using the TI isolated word database for 16 speakers demonstrated degradation in performance when weight quantization fell below 8 bits. The competitive nature of the algorithm rela..xes constraints on uniformity and linearity which makes it an excellent candidate for a fully analog circuit implementation. Prototype circuits have been fabricated and characterized following the constraints established through the simulation efforts. 1 Introduction The self-organizing feature map algorithm developed by Kohonen [Kohonen, 1988] readily lends itself to the task of vector quantization for use in such areas as speech recognition.

It is generally admitted that generalization performance of back-propagation networks (Rumelhart, Hinton & Williams, 1986) will depend on the relative size ofthe training data and of the trained network. By analogy to curve-fitting and for theoretical considerations, the generalization performance of the network should decrease as the size of the network and the associated number of degrees of freedom increase (Rumelhart, 1987; Denker et al., 1987; Hanson & Pratt, 1989). This paper examines the dynamics of the standard back-propagation algorithm (BP) and of a constrained back-propagation variation (CBP), designed to adapt the size of the network to the training data base. The performance, learning dynamics and the representations resulting from the two algorithms are compared.

The mapping of the back-propagation and mean field theory learning algorithms onto a generic 2-D SIMD computer is described. This architecture proves to be very adequate for these applications since efficiencies close to the optimum can be attained. Expressions to find the learning rates are given and then particularized to the DAP array procesor.

In Daniel G. Bobrow and Alan Model of Computation Based on a Calculus University of New York at Albany, 1986. On the of many sorted interpolation theorems. An investigation [Höhfeld and Smolka, 1988] Markus Höhfeld in Expert Systems III, pages 184-194, into inference with restricted and G. Smolka. A many-sorted resolution based Levesque, and Raymond Reiter, editors, 2(3):142-150, 1986. An overview in a topically organized semantic of the HORNE logic programming system.

The growth in the amount of available databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization.