A new optimization strategy, Mean Field Annealing, is presented. Its application to MAP restoration of noisy range images is derived and experimentally verified.

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

A great deal of interest has recently been focused on theories concerning parallel distributed processing in central nervous systems. In particular, many researchers have become very interested in the structure and function of "computational maps" in sensory systems. As defined in a recent review (Knudsen et al, 1987), a "map" is an array of nerve cells, within which there is a systematic variation in the "tuning" of neighboring cells for a particular parameter. For example, the projection from retina to visual cortex is a relatively simple topographic map; each cortical hypercolumn itself contains a more complex "computational" map of preferred line orientation representing the angle of tilt of a simple line stimulus. The overall goal of the research in my lab is to determine how a relatively complex mapped sensory system extracts and encodes information from external stimuli.

Howard and M. A. Huckvale, "Acoustic-phonetic attribute determination using multi-layer perceptrons", IEEE Colloquium Digest 1988/11.

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.

ABSTRACT We discuss synthetic receptors for haptic sensing. These are based on magnetic field sensors (Hall effect structures) fabricated using standard CMOS technologies. These receptors, biased with a small permanent magnet can detect the presence of ferro or ferri-magnetic objects in the vicinity of the sensor. They can also detect the magnitude and direction of the magnetic field. INTRODUCTION The organizational structure and functioning of the sensory periphery in living beings has always been the subject of extensive research.

Hardware implementation of neuromorphic algorithms is hampered by high degrees of connectivity. Functionally equivalent feedforward networks may be formed by using limited fan-in nodes and additional layers.

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 in the thresholds of MOSFET devices and is local in character. The network 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.

However, to achieve the full power of a VLSI implementation of an adaptive algorithm, the learning operation must built into the circuit. We have fabricated and tested a circuit ideal for this purpose by connecting a pair of capacitors with a CCD like structure, allowing for variable size weight changes as well as a weight decay operation. A 2.51-' CMOS version achieves better than 10 bits of dynamic range in a 140/'

We have fabricated a test chip in 2 micron CMOS technology that embodies these ideas and we report our evaluation of the microchip and our plans for improvements. Knowledge is encoded in the test chip by presenting digital patterns to it that are examples of a desired input-output Boolean mapping. This knowledge is learned and stored entirely on chip in a digitally controlled synapse-like element in the form of connection strengths between neuron-like elements. The only portion of this learning system which is off chip is the VLSI test equipment used to present the patterns. This learning system uses a modified Boltzmann machine algorithm[3] which, if simulated on a serial digital computer, takes enormous amounts of computer time. Our physical implementation is about 100,000 times faster. The test chip, if expanded to a board-level system of thousands of neurons, would be an appropriate architecture for solving artificial intelligence problems whose solutions are hard to specify using a conventional rule-based approach. Examples include speech and pattern recognition and encoding some types of expert knowledge.