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.

Sound localization by the barn owl (tyto alba) measured with the search coil technique.

University of Rochester) and ICSIM (lCSI Berkeley) allow the definition of unit types and complex connectivity patterns. On a very high level of abstraction, simulators like tleam (UCSD) allow the easy realization of predefined network architectures (feedforwardnetworks) and leaming algorithms such as backpropagation. Ben Gomes, International Computer Science Institute (Berkeley) introduced the Connectionist Supercomputer 1. The CNSl is a multiprocessor system designed for moderate precision fixed point operations used extensively in connectionist network calculations. Custom VLSI digital processors employ an on-chip vector coprocessor unit tailored for neural network calculations and controlled by RISC scalar CPU. One processor and associated commercial DRAM comprise a node, which is connected in a mesh topology with other nodes to establish a MIMD array. One edge of the communications meshis reserved for attaching various 110 devices, which connect via a custom network adaptor chip. The CNSl operates as a compute server and one 110 port is used for connecting to a host workstation. Users with mainstream connectionist applications can use CNSim, an object-oriented, graphical high-level interface to the CNSl environment.

Four versions of a k-nearest neighbor algorithm with locally adaptive kare introduced and compared to the basic k-nearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross-validation computations in the local neighborhood of the query. Local kNN methods are shown to perform similar to kNN in experiments with twelve commonly used data sets. Encouraging resultsin three constructed tasks show that local methods can significantly outperform kNN in specific applications. Local methods can be recommended for online learning and for applications wheredifferent regions of the input space are covered by patterns solving different sub-tasks.

Department of Computer Science University of Rochester Rochester, NY 14627 Abstract One of the advantages of supervised learning is that the final error metric isavailable during training. For classifiers, the algorithm can directly reduce the number of misclassifications on the training set. Unfortunately, whenmodeling human learning or constructing classifiers for autonomous robots,supervisory labels are often not available or too expensive. In this paper we show that we can substitute for the labels by making use of structure between the pattern distributions to different sensory modalities.We show that minimizing the disagreement between the outputs of networks processing patterns from these different modalities is a sensible approximation to minimizing the number of misclassifications in each modality, and leads to similar results. Using the Peterson-Barney vowel dataset we show that the algorithm performs well in finding appropriate placementfor the codebook vectors particularly when the confuseable classes are different for the two modalities. 1 INTRODUCTION This paper addresses the question of how a human or autonomous robot can learn to classify new objects without experience with previous labeled examples.

The acquisition of spatial knowledge by exploration of an environment has been the subject ofseveral recent experimental studies.

The feature correspondence problem is a classic hurdle in visual object-recognition concerned with determining the correct mapping between the features measured from the image and the features expected bythe model. In this paper we show that determining good correspondences requires information about the joint probability density over the image features. We propose "likelihood based correspondence matching" as a general principle for selecting optimal correspondences.The approach is applicable to nonrigid models, allows nonlinear perspective transformations, and can optimally dealwith occlusions and missing features.

In a talk entitled "Trajectory Control of Convergent Networks with applications to TSP", Natan Peterfreund (Computer Science, Technion) dealt with the problem of controlling the trajectories of continuous convergent neural networks models for solving optimization problems, without affecting their equilibria set and their convergence properties.Natan presented a class of feedback control functions which achieve this objective, while also improving the convergence rates. A modified Hopfield andTank neural network model, developed through the proposed feedback approach, was found to substantially improve the results of the original model in solving the Traveling Salesman Problem. The proposed feedback overcame the 2n symmetric property of the TSP problem. In a talk entitled "Training Feedforward Neural Networks quickly and accurately using Very Fast Simulated Reannealing Methods", Bruce Rosen (Asst. Professor, Computer Science, UT San Antonio) presented the Very Fast Simulated Reannealing (VFSR)algorithm for training feedforward neural networks [2].

Catastrophic interference in connectionist networks: Can it be predicted, can it be prevented? Catastrophic forgetting occurs when connectionist networks learn new information, and by so doing, forget all previously learned information. This workshop focused primarily on the causes of catastrophic interference, the techniques that have been developed to reduce it, the effect of these techniques on the networks' ability to generalize, andthe degree to which prediction of catastrophic forgetting is possible. The speakers were Robert French, Phil Hetherington (Psychology Department, McGill University, het@blaise.psych.mcgill.ca), French indicated that catastrophic forgetting is at its worst when high representation overlapat the hidden layer combines with significant teacher-output error.