Published by AlP Press, New York, N.Y (1988).

This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.

I will describe my recent results on the automatic development of fixedwidth recursivedistributed representations of variable-sized hierarchal data structures. One implication of this wolk is that certain types of AIstyle data-structures can now be represented in fixed-width analog vectors. Simple inferences can be perfonned using the type of pattern associations that neural networks excel at Another implication arises from noting that these representations become self-similar in the limit Once this door to chaos is opened.

We have mathematically shown that cortical maps in the primary sensory cortices can be reproduced by using three hypotheses which have physiological basis and meaning. Here, our main focus is on ocular.dominance

ALVIS is a reinforcement-based connectionist architecture that learns associative maps in continuous multidimensional environments. Thediscovered locations of positive and negative reinforcements arerecorded in "do be" and "don't be" subnetworks, respectively. The outputs of the subnetworks relevant to the current goalare combined and compared with the current location to produce an error vector. This vector is backpropagated through a motor-perceptual mapping network.

System) is a neural network with complex dynamical properties.

The weakly electric fish, Gnathonemus peters;;, explores its environment by generating pulsedelecbic fields and detecting small pertwbations in the fields resulting from nearby objects. Accordingly, the fISh detects and discriminates objects on the basis of a sequence of elecbic "images" whose temporal and spatial properties depend on the timing ofthe fish's electric organ discharge and its body position relative to objects in its environmenl Weare interested in investigating how these fish utilize timing and body-position during exploration to aid in object discrimination. We have developed a fmite-element simulation of the fish's self-generated electric fields so as to reconstruct the electrosensory consequencesof body position and electric organ discharge timing in the fish. This paper describes this finite-element simulation system and presents preliminary electric fieldmeasurements which are being used to tune the simulation.

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.

We have tested this assumption in the hippocampus, a cortical structure or the brain that is involved in long-term memory. A brier, high-frequency activation or excitatory synapses in the hippocampus produces an increase in synaptic strength known as long-term potentiation, or LTP (BUss and Lomo, 1973), that can last ror many days. LTP is known to be Hebbian since it requires the simultaneous release or neurotransmitter from presynaptic terminals coupled with postsynaptic depolarization (Kelso et al, 1986; Malinow and Miller, 1986; Gustatrson et al, 1987). However, a mechanism ror the persistent reduction or synaptic strength that could balance LTP has not yet been demonstrated. We studied theassociative interactions between separate inputs onto the same dendritic trees or hippocampal pyramidal cells or field CAl, and round that a low-frequency input which, by itselr, does not persistently change synaptic strength, can either increase (associative LTP) or decrease in strength (associative long-term depression or LTD) depending upon whether it is positively or negatively correlated in time with a second, high-frequency bursting input. LTP or synaptic strength is Hebbian, and LTD is anti-Hebbian since it is elicited by pairing presynaptic firing with postsynaptic hyperpolarizationsufficient to block postsynaptic activity.

In recent years connectionist (sometimes called "non-symbolic"