Humans use visual as well as auditory speech signals to recognize spoken words. A variety of systems have been investigated for performing thistask. The main purpose of this research was to systematically comparethe performance of a range of dynamic visual features on a speechreading task. We have found that normalization ofimages to eliminate variation due to translation, scale, and planar rotation yielded substantial improvements in generalization performanceregardless of the visual representation used. In addition, the dynamic information in the difference between successive framesyielded better performance than optical-flow based approaches, and compression by local low-pass filtering worked surprisingly betterthan global principal components analysis (PCA). These results are examined and possible explanations are explored.

An electrmic model can be especially helpful, when the parameters of the model can be easily controlled, and when the model will operate in real time.

We describe the implementation of a hidden Markov model state decoding system, a component for a wordspotting speech recognition system.The key specification for this state decoder design is microwatt power dissipation; this requirement led to a continuoustime, analogcircuit implementation. We characterize the operation of a 10-word (81 state) state decoder test chip.

The major problem that has prevented practical application of analog neuro-LSIs has been poor accuracy due to fluctuating analog device characteristics inherent in each device as a result of manufacturing. This paper proposes a dynamic control architecture that allows analog silicon neural networks to compensate for the fluctuating device characteristics and adapt to a change in input DC level. We have applied this architecture to compensate for input offset voltages of an analog CMOS WTA (Winner-Take-AlI) chip that we have fabricated. Experimental data show the effectiveness of the architecture.

A one-dimensional visual tracking chip has been implemented using neuromorphic,analog VLSI techniques to model selective visual attention in the control of saccadic and smooth pursuit eye movements. Thechip incorporates focal-plane processing to compute image saliency and a winner-take-all circuit to select a feature for tracking. The target position and direction of motion are reported as the target moves across the array. We demonstrate its functionality ina closed-loop system which performs saccadic and smooth pursuit tracking movements using a one-dimensional mechanical eye. 1 Introduction Tracking a moving object on a cluttered background is a difficult task. When more than one target is in the field of view, a decision must be made to determine which target to track and what its movement characteristics are.

Many popular learning rules are formulated in terms of continuous, analoginputs and outputs. Biological systems, however, use action potentials, which are digital-amplitude events that encode analog information in the inter-event interval. Action-potential representations are now being used to advantage in neuromorphic VLSI systems as well. We report on a simple learning rule, based on the Riccati equation described by Kohonen [1], modified for action-potential neuronal outputs. We demonstrate this learning rule in an analog VLSI chip that uses volatile capacitive storage for synaptic weights. We show that our time-dependent learning rule is sufficient to achieve approximate weight normalization and can detect temporal correlations in spike trains.

Two dimensional image motion detection neural networks have been implemented using a general purpose analog neural computer. The neural circuits perform spatiotemporal feature extraction based on the cortical motion detection model of Adelson and Bergen. The neural computer provides the neurons, synapses and synaptic time-constants required to realize the model in VLSI hardware. Results show that visual motion estimation can be implemented with simple sum-andthreshold neuralhardware with temporal computational capabilities. The neural circuits compute general 20 visual motion in real-time.

We present a theoretical framework for population codes which generalizes naturally to the important case where the population provides information about a whole probability distribution over an underlying quantity rather than just a single value. We use the framework to analyze two existing models, and to suggest and evaluate a third model for encoding such probability distributions. 1 Introduction Population codes, where information is represented in the activities of whole populations ofunits, are ubiquitous in the brain. There has been substantial work on how animals should and/or actually do extract information about the underlying encoded quantity.

Optimal Brain Damage (OBD) is a method for reducing the number ofweights in a neural network. OBD estimates the increase in cost function if weights are pruned and is a valid approximation if the learning algorithm has converged into a local minimum. On the other hand it is often desirable to terminate the learning process beforea local minimum is reached (early stopping). In this paper we show that OBD estimates the increase in cost function incorrectly if the network is not in a local minimum. We also show how OBD can be extended such that it can be used in connection withearly stopping.

We seek to analyze and manipulate two factors, which we call style and content, underlying a set of observations. We fit training data with bilinear models which explicitly represent the two-factor structure. Thesemodels can adapt easily during testing to new styles or content, allowing us to solve three general tasks: extrapolation of a new style to unobserved content; classification of content observed in a new style; and translation of new content observed in a new style.