In this paper, we discuss the nature of the time dependence currently employed in our systems using recurrent networks (RNs) and feed-forward multi-layer perceptrons (MLPs). In particular, we introduce local recurrences into a MLP to produce an enhanced input representation. This is in the form of an adaptive gamma filter and incorporates an automatic approach for learning temporal dependencies. We have experimented on a speakerindependent phonerecognition task using the TIMIT database. Results using the gamma filtered input representation have shown improvement over the baseline MLP system. Improvements have also been obtained through merging the baseline and gamma filter models.

We apply HMMs to the problem of modeling exons, intronsand detecting splice sites in the human genome. Our most interesting result so far is the detection of particular oscillatory patterns,with a minimal period ofroughly 10 nucleotides, that seem to be characteristic of exon regions and may have significant biological implications.

The biological network is small and relatively well understood, and the silicon model can therefore span three levels of organization in the leech nervous system (neuron, ganglion, system); it represents one of the first comprehensive models of leech swimming operating in real-time. The circuit employs biophysically motivated analog neurons networked to form multiple biologically inspired silicon ganglia. These ganglia are coupled using known interganglionic connections. Thus the model retains the flavor of its biological counterpart, and though simplified, the output of the silicon circuit is similar to the output of the leech swim central pattern generator. The model operates on the same time-and spatial-scale as the leech nervous system and will provide an excellent platform with which to explore real-time adaptive locomotion in the leech and other "simple" invertebrate nervous systems.

H. Schwarze Dept. of Theoretical Physics Lund University Solvegatan 14A 223 62 Lund Sweden J.Hertz Nordita Blegdamsvej 17 2100 Copenhagen 0 Denmark Abstract The problem of learning from examples in multilayer networks is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error of a fully connected committee machine in the limit of a large number of hidden units. If the number of training examples is proportional to the number of inputs in the network, the generalization error as a function of the training set size approaches a finite value. If the number of training examples is proportional to the number of weights in the network we find first-order phase transitions with a discontinuous drop in the generalization error for both binary and continuous weights. 1 INTRODUCTION Feedforward neural networks are widely used as nonlinear, parametric models for the solution of classification tasks and function approximation. Trained from examples of a given task, they are able to generalize, i.e. to compute the correct output for new, unknown inputs.

We study feed-forward nets with arbitrarily many layers, using the standard sigmoid,tanh x. Aside from technicalities, our theorems are: 1. Complete knowledge of the output of a neural net for arbitrary inputs uniquely specifies the architecture, weights and thresholds; and 2. There are only finitely many critical points on the error surface for a generic training problem. Neural nets were originally introduced as highly simplified models of the nervous system. Today they are widely used in technology and studied theoretically by scientists from several disciplines. However, they remain little understood.

Urs A. Miiller, Michael Kocheisen, and Anton Gunzinger Electronics Laboratory, Swiss Federal Institute of Technology CH-B092 Zurich, Switzerland Abstract The performance requirements in experimental research on artificial neuralnets often exceed the capability of workstations and PCs by a great amount. But speed is not the only requirement. Flexibility and implementation time for new algorithms are usually of equal importance. This paper describes the simulation of neural nets on the MUSIC parallel supercomputer, a system that shows a good balance between the three issues and therefore made many research projects possible that were unthinkable before. The system should be flexible, simple to program and the realization time should be short enough to not have an obsolete system by the time it is finished.

Smirnakis Lyman Laboratory of Physics Harvard University Cambridge, MA 02138 Lei Xu * Dept. of Computer Science HSH ENG BLDG, Room 1006 The Chinese University of Hong Kong Shatin, NT Hong Kong Abstract Recent work by Becker and Hinton (Becker and Hinton, 1992) shows a promising mechanism, based on maximizing mutual information assumingspatial coherence, by which a system can selforganize itself to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories ofvisual perception and to other organization principles for early vision (Atick and Redlich, 1990). Methods for implementation usingvariants of stochastic learning are described and, for the special case of linear filtering, we derive an analytic expression for the output. 1 Introduction The input intensity patterns received by the human visual system are typically complicated functions of the object surfaces and light sources in the world. It *Lei Xu was a research scholar in the Division of Applied Sciences at Harvard University while this work was performed. Thus the visual system must be able to extract information from the input intensities that is relatively independent of the actual intensity values.

We report face recognition results under drastically changing lighting conditions for a computer vision system whichconcurrently uses a contrast sensitive silicon retina and a conventional, gaincontrolled CCO camera. For both input devices the face recognition system employs an elastic matching algorithm with wavelet based features to classify unknown faces. To assess the effect of analog on-chip preprocessing by the silicon retina the CCO images have been "digitally preprocessed" with a bandpass filter to adjust the power spectrum. Thesilicon retina with its ability to adjust sensitivity increases the recognition rate up to 50 percent. These comparative experiments demonstrate that preprocessing with an analog VLSI silicon retina generates imagedata enriched with object-constant features.

What is the'correct' theoretical description of neuronal activity? The analysis of the dynamics of a globally connected network of spiking neurons (the Spike Response Model) shows that a description bymean firing rates is possible only if active neurons fire incoherently. Iffiring occurs coherently or with spatiotemporal correlations, the spike structure of the neural code becomes relevant. Alternatively, neurons can be gathered into local or distributed ensembles or'assemblies'. A description based on the mean ensemble activity is, in principle, possible but the interaction between different assembliesbecomes highly nonlinear. A description with spikes should therefore be preferred.

Thomas G. Dietterich Arris Pharmaceutical Corporation and Oregon State University Corvallis, OR 97331-3202 Ajay N. Jain Arris Pharmaceutical Corporation 385 Oyster Point Blvd., Suite 3 South San Francisco, CA 94080 Richard H. Lathrop and Tomas Lozano-Perez Arris Pharmaceutical Corporation and MIT Artificial Intelligence Laboratory 545 Technology Square Cambridge, MA 02139 Abstract In drug activity prediction (as in handwritten character recognition), thefeatures extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In handwritten characterrecognition, one of the best techniques for addressing thisproblem is the tangent distance method of Simard, LeCun and Denker (1993). Jain, et al. (1993a; 1993b) introduce a new technique-dynamic reposing-that also addresses this problem. Dynamicreposing iteratively learns a neural network and then reposes the examples in an effort to maximize the predicted output values.New models are trained and new poses computed until models and poses converge. This paper compares dynamic reposing to the tangent distance method on the task of predicting the biological activityof musk compounds.