The bam owl has fused visual/auditory/motor representations of space in its midbrain which are used to orient the head so that visual orauditory stimuli are centered in the visual field of view.

K. Y. Goldberg and B. A. Pearlmutter School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT Computing the inverse dynamics of a robot ann is an active area of research in the control literature. We hope to learn the inverse dynamics by training a neural network on the measured response of a physical ann. The input to the network is a temporal window of measured positions; output is a vector of torques. We train the network on data measured from the first two joints of the CMU Direct-Drive Arm II as it moves through a randomly-generated sample of "pick-and-place" trajectories. We then test generalization with a new trajectory and compare its output with the torque measured at the physical arm.

In the developing visual system in many mammalian species, there is initially a uniform, overlappinginnervation of layer 4 of the visual cortex by inputs representing the two eyes. Subsequently, these inputs segregate into patches or stripes that are largely or exclusively innervated by inputs serving a single eye, known as ocular dominance patches. The ocular dominance patches are on a small scale compared to the map of the visual world, so that the initially continuous map becomes two interdigitated maps, one representing each eye. These patches, together with the layers of cortex above and below layer 4, whose responses are dominated by the eye innervating the corresponding layer 4 patch, are known as ocular dominance columns.

ABSTRACT A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near-or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics that proved to share a common framework. The fIrst topic, statistical prediction, is the task of associating extremely large perceptual state vectors with future events.

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perfOIm the task differs dramatically when the networlc is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner, using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

A large fraction of recent work in artificial neural nets uses multilayer perceptrons trained with the back-propagation algorithm described by Rumelhart et.

While we are waiting for the ultimate biophysics of cell membranes and synapses to be completed, we may speculate on the shapes of neurons and on the patterns of their connections. Much of this will be significant whatever the outcome of future physiology. Take as an example the isotropy, anisotropy and periodicity of different kinds of neural networks. The very existence of these different types in different parts of the brain (or in different brains) defeats explanation in terms of embryology; the mechanisms of development are able to make one kind of network or another. The reasons for the difference must be in the functions they perform.

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

ABSTRACf The Space Environment Laboratory in Boulder has collaborated with the University of Colorado to construct a small expert system for solar flare forecasting, called THEa. It performed as well as a skilled human forecaster. We have constructed TheoNet, a three-layer back-propagation connectionist network that learns to forecast flares as well as THEa does. TheoNet's success suggests that a connectionist network can perform the task of knowledge engineering automatically. A study of the internal representations constructed by the network may give insights to the "microstructure" of reasoning processes in the human brain.