The first case shows recurrent activity, while the second case is non-recurrent or feed forward. The polarity of these terms signify excitatory or inhibitory interactions. Shunting network equations can be derived from various sources such as the passive membrane equation with synaptic interaction (Grossberg 1973, Pinter 1983), models of dendritic interaction (RaIl 1977), or experiments on motoneurons (Ellias and Grossberg 1975). While the exact mechanisms of synaptic interactions are not known in every individual case,neurobiological evidence of shunting interactions appear in several 696 Nabet, Darling and Pinter areas such as sensory systems, cerebellum, neocortex, and hippocampus (Grossberg 1973, Pinter 1987). In addition to neurobiology, these networks have been used to successfully explain data from disciplines ranging from population biology (Lotka 1956) to psychophysics and behavioral psychology (Grossberg 1983). Shunting nets have important advantages over additive models which lack the extra nonlinearityintroduced by the multiplicative terms.

ABSTRACT We describe pulse - stream firing integrated circuits that implement asynchronousanalog neural networks. Synaptic weights are stored dynamically, and weighting uses time-division of the neural pulses from a signalling neuron to a receiving neuron. MOS transistors in their "ON" state act as variable resistors to control a capacitive discharge, and time-division is thus achieved by a small synapse circuit cell. The VLSI chip set design uses 2.5J.1.m INTRODUCTION Neural network implementations fall into two broad classes - digital [1,2] and analog (e.g. The strengths of a digital approach include the ability to use well-proven design techniques, high noise immunity, and the ability to implement programmable networks.

Popular neuron models are based upon some statistical measure of known natural behavior. Whether that measure is expressed in terms of average firing rate or a firing probability, the instantaneous neuron activation is only represented in an abstract sense. Artificial electronic neurons derived from these models represent this excitation level as a binary code or a continuous voltage at the output of a summing amplifier. While such models have been shown to perform well for many applications, andform an integral part of much current work, they only partially emulate the manner in which natural neural networks operate. They ignore, for example, differences in relative arrival times of neighboring action potentials -- an important characteristic known to exist in natural auditory and visual networks {Sejnowski, 1986}. They are also less adaptable to fme-grained, neuron-centered learning, like the post-tetanic facilitation observed in natural neurons. We are investigating the implementation and application of neuron circuits which better approximate natural neuron function.

ABSTRACT The Parsing and Learning System(PALS) is a massively parallel self-tuning context-free parser. It is capable of parsing sentences of unbounded length mainly due to its parse-tree representation scheme. The system is capable of improving its parsing performance through the presentation of training examples. INTRODUCTION Recent PDP research[Rumelhart et al.- 1986; Feldman and Ballard, 1982; Lippmann, 1987] involving natural language processtng[Fanty, 1988; Selman, 1985; Waltz and Pollack, 1985] have unrealistically restricted sentences to a fixed length. A solution to this problem was presented in the system CONPARSE[Charniak and Santos.

The application of neural networks to the demodulation of spread-spectrum signals in a multiple-access environment is considered. This study is motivated in large part by the fact that, in a multiuser system, the conventional (matched filter) receiversuffers severe performance degradation as the relative powers of the interfering signals become large (the "near-far" problem). Furthermore, the optimum receiver, which alleviates the near-far problem, is too complex to be of practical use. Receivers based on multi-layer perceptrons are considered as a simple and robust alternative to the optimum solution.The optimum receiver is used to benchmark the performance of the neural net receiver; in particular, it is proven to be instrumental in identifying the decision regions of the neural networks. The back-propagation algorithm and a modified version of it are used to train the neural net. An importance sampling technique is introduced to reduce the number of simulations necessary to evaluate the performance of neural nets.

Linsker's model was presented with input patterns each consisting

Valentino Braitenberg Max Planck Institute Federal Republic of Germany 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.

We analyze a mathematical model for retinal directionally selective cells based on recent electrophysiological data, and show that its computation of motion direction is robust against noise and speed.

Visual guidance of a multi-link arm through a cluttered workspace is known to be an extremely difficult computational problem. Classical approaches in the field of robotics have typically broken the problem into pieces of manageable size, including modules for direct and inverse kinematics and dynamics [7], along with a variety of highly complex algorithms for motion planning in the configuration space of a multi-link arm (e.g.

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/'