The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neuronal structure. This model, however, is based on highly artificial assumptions, especially the use of formal-two state neurons (Hopfield,1982) or graded-response neurons (Hopfield, 1984).

The main point of this paper is that stochastic neural networks have a mathematical structure that corresponds quite closely with that of quantum field theory. Neural network Liouvillians and Lagrangians can be derived, just as can spin Hamiltonians and Lagrangians in QFf. It remains to show the efficacy of such a description.

A high speed implementation of the CMAC neural network was designed using dedicated CMOS logic. This technology was then used to implement two general purpose CMAC associative memory boards for the VME bus. Each board implements up to 8 independent CMAC networks with a total of one million adjustable weights. Each CMAC network can be configured to have from 1 to 512 integer inputs and from 1 to 8 integer outputs. Response times for typical CMAC networks are well below 1 millisecond, making the networks sufficiently fast for most robot control problems, and many pattern recognition and signal processing problems.

In 4 of these methods the gradient is divided component-wise by a decaying average of either the second derivatives or their absolute values.

We study the evolution of the generalization ability of a simple linear perceptron withN inputs which learns to imitate a "teacher perceptron". The system is trained on p aN binary example inputs and the generalization abilitymeasured by testing for agreement with the teacher on all 2N possible binary input patterns. The dynamics may be solved analytically and exhibits a phase transition from imperfect to perfect generalization at a 1. Except at this point the generalization ability approaches its asymptotic value exponentially, with critical slowing down near the transition; therelaxation time is ex (1 - y'a)-2.

Bruno Caprile I.R.S.T. Povo, Italy, 38050 Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multidimensional function.

One current aim of molecular biology is determination of the (3D) tertiary structures ofproteins in their folded native state from their sequences of amino acid 523 524 Fredholm, Bohr, Bohr, Brunak, Cotterill, Lautrup, and Thtersen residues. Since Kendrew & Perutz solved the first protein structures, myoglobin and hemoglobin, and explained from the discovered structures how these proteins perform their function, it has been widely recognized that protein function is intimately linkedwith protein structure[l]. Within the last two decades X-ray crystallographers have solved the 3-dimensional (3D) structures of a steadily increasing number of proteins in the crystalline state, and recently 2D-NMR spectroscopy has emerged as an alternative method for small proteins in solution. Today approximately three hundred 3D structures have been solved by these methods, although only about half of them can be considered as truly different, and only around a hundred of them are solved at high resolution (that is, less than 2A). The number of protein sequences known today is well over 20,000, and this number seems to be growing at least one order of magnitude faster than the number of known 3D protein structures. Obviously, it is of great importance to develop tools that can predict structural aspects of proteins on the basis of knowledge acquired from known 3D structures.

This work addresses three problems with reinforcement learning and adaptive neuro-control:1.

Barto, Sutton and Watkins [2] introduced a grid task as a didactic example oftemporal difference planning and asynchronous dynamical pre gramming. Thispaper considers the effects of changing the coding of the input stimulus, and demonstrates that the self-supervised learning of a particular form of hidden unit representation improves performance.

There have been very few demonstrations ofthe application ofVLSI neural networks to real world problems. Yet there are many signal processing, pattern recognition or optimization problems where a large number of competing hypotheses need to be explored in parallel, most often in real time. The massive parallelism of VLSI neural network devices, with one multiplier circuit per synapse, is ideally suited to such problems. In this paper, we present preliminary results from our design for a real time robot navigation system based on VLSI neural network modules. This is a - Also: RSRE, Great Malvern, Worcester, WR14 3PS 422 Real-time Autonomous Robot Navigation Using VLSI Neural Networks 423 real world problem which has not been fully solved by traditional AI methods; even when partial solutions have been proposed and implemented, these have required vast computational resources, usually remote from the robot and linked to it via an umbilical cord. 2 OVERVIEW The aim of our work is to develop an autonomous vehicle capable of real-time navigation, including obstacle avoidance, in a known indoor environment.