Jared Leinbach Deparunent of Psychology Carnegie-Mellon University Pittsburgh, PA 15213 ABSTRACT This research involves a method for finding global maxima in constraint satisfaction networks. It is an annealing process butt unlike most otherst requires no annealing schedule. Temperature is instead determined locally by units at each updatet and thus all processing is done at the unit level. There are two major practical benefits to processing this way: 1) processing can continue in'badt areas of the networkt while'goodt areas remain stablet and 2) processing continues in the'bad t areast as long as the constraints remain poorly satisfied (i.e. it does not stop after some predetermined number of cycles). As a resultt this method not only avoids the kludge of requiring an externally determined annealing schedulet but it also finds global maxima more quickly and consistently than externally scheduled systems (a comparison to the Boltzmann machine (Ackley et alt 1985) is made).

The concept of the stochastic Boltzmann machine (BM) is auractive for decision making and pattern classification purposes since the probability of attaining the network states is a function of the network energy. Hence, the probability of attaining particular energy minima may be associated with the probabilities of making certain decisions (or classifications). However, because of its stochastic nature, the complexity of the BM is fairly high and therefore such networks are not very likely to be used in practice. In this paper we suggest a way to alleviate this drawback by converting the stochastic BMinto a deterministic network which we call the Boltzmann Perceptron Network(BPN). The BPN is functionally equivalent to the BM but has a feed-forward structure and low complexity.

An improved learning paradigm that offers a significant reduction in computation timeduring the supervised learning phase is described. It is based on extending the role that the neuron plays in artificial neural systems. Prior work has regarded the neuron as a strictly passive, nonlinear processing element, and the synapse on the other hand as the primary source of information processing and knowledge retention. In this work, the role of the neuron is extended insofar as allowing itsparameters to adaptively participate in the learning phase. The temperature of the sigmoid function is an example of such a parameter.

This heterogeneity is crucial to the flexible generation of behavior which is essential for survival in a complex, dynamic environment. It may also provide powerful insights into the design of artificial neural networks. In this paper, we describe a heterogeneous neural network for controlling the wa1king of a simulated insect. This controller is inspired by the neuroethological and neurobiological literature on insect locomotion. It exhibits a variety of statically stable gaits at different speeds simply by varying the tonic activity of a single cell. It can also adapt to perturbations as a natural consequence of its design. INTRODUCTION Even very simple animals exhibit a dazzling variety of complex behaviors which they continuously adapt to the changing circumstances of their environment. Nervous systems evolved in order to generate appropriate behavior in dynamic, uncertain situations and thus insure the survival of the organisms containing them.

We propose a new neural network structure that is compatible with silicon technology and has built-in learning capability. The thrust of this network work is a new synapse function. The synapses have the feature that the learning parameter is embodied inthe thresholds of MOSFET devices and is local in character. Thenetwork is shown to be capable of learning by example as well as exhibiting the desirable features of the Hopfield type networks. The thrust of what we want to discuss is a new synapse function for an artificial neuron to be used in a neural network.

Reconstructing a surface from sparse sensory data is a well-known problem iIi computer vision. This paper describes an experimental analog VLSI chip for smooth surface interpolation from sparse depth data. An eight-node ID network was designed in 3J.lm CMOS and successfully tested.

Otherwise it might ascribe different characteristics to the same object under different lights. But the first step in using color for recognition, segmentingthe scene into regions of different colors, does not require color constancy.

In this paper we explore this idea by examining the function a simple cerebellar-related behavior, the vestibula-ocular reflex or VOR, in which eye movements are generated to minimize image slip on the retina during rapid head movements. Considering this system from the point of view of statistical estimation theory, our results suggest of the VOR, often regarded as a static orthat the transfer function slowly modifiable feature of the system, should actually be continuously and rapidly changed during head movements. We further suggest that these changes are under the direct control of the cerebellar cortex and propose experiments to test this hypothesis.

The network uses the pattern of activation over a set of hidden units from time-step tl, together with element t, to predict element t 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. Cluster analyses of the hidden-layer patterns of activation showed that they encode prediction-relevant information about the entire path traversed through the network. We illustrate the phases of learning with cluster analyses performed at different points during training. Several connectionist architectures that are explicitly constrained to capture sequential infonnation have been developed. Examples are Time Delay Networks (e.g.

We introduce an optimization approach for solving problems in computer visionthat involve multiple levels of abstraction. Our objective functions include compositional and specialization hierarchies. We cast vision problems as inexact graph matching problems, formulate graph matching in terms of constrained optimization, and use analog neural networks to perform the optimization. The method is applicable to perceptual groupingand model matching. Preliminary experimental results are shown.