Olshausen Department of Anatomy and Neurobiology Washington University School of Medicine St. Louis, MO 63110 The purpose of this workshop was to discuss both recent experimental findings and computational models of the neurobiological implementation of selective attention. Recent experimental results were presented in two of the four presentations given (C.E. Connor, Washington University and B.C. Motter, SUNY and V.A. Medical Center, Syracuse), while the other two talks were devoted to computational models (E. Connor presented the results of an experiment in which the receptive field profiles of V 4 neurons were mapped during different states of attention in an awake, behaving monkey. The attentional focus was manipulated in this experiment by altering the position of a behaviorally relevant ring-shaped stimulus.

Functional complexity of a software module can be measured in terms of static complexity metrics of the program text. Classifying softwaremodules, based on their static complexity measures, into different fault-prone categories is a difficult problem in software engineering.This research investigates the applicability of neural network classifiers for identifying fault-prone software modules usinga data set from a commercial software system. A preliminary empiricalcomparison is performed between a minimum distance based Gaussian classifier, a perceptron classifier and a multilayer layer feed-forward network classifier constructed using a modified Cascade-Correlation algorithm. The modified version of the Cascade-Correlation algorithm constrains the growth of the network size by incorporating a cross-validation check during the output layer training phase. Our preliminary results suggest that a multilayer feed-forward network can be used as a tool for identifying fault-pronesoftware modules early during the development cycle. Other issues such as representation of software metrics and selection of a proper training samples are also discussed.

Online cursive handwriting recognition is currently one of the most intriguing challenges in pattern recognition. This study presents a novel approach to this problem which is composed of two complementary phases.The first is dynamic encoding of the writing trajectory into a compact sequence of discrete motor control symbols. In this compact representation we largely remove the redundancy of the script, while preserving most of its intelligible components. In the second phase these control sequences are used to train adaptive probabilistic acyclic automata (PAA) for the important ingredients of the writing trajectories, e.g.

We propose a new distance measure which is optimal for the task of local PCA. Our results with speech and image data indicate that the nonlinear techniques provide more accurate encodings than PCA. Our local linear algorithm produces more accurate encodings (except for one simulation with image data), and trains much faster than five layer auto-associative networks. Acknowledgments This work was supported by grants from the Air Force Office of Scientific Research (F49620-93-1-0253) and Electric Power Research Institute (RP8015-2). The authors are grateful to Gary Cottrell and David DeMers for providing their image database and clarifying their experimental results. We also thank our colleagues in the Center for Spoken Language Understanding at OGI for providing speech data.

We present a neural network simulation which we implemented on the massively parallel Connection Machine 2. In contrast to previous work, this simulator is based on biologically realistic neurons withnontrivial single-cell dynamics, high connectivity with a structure modelled in agreement with biological data, and preservation ofthe temporal dynamics of spike interactions. We simulate neural networks of 16,384 neurons coupled by about 1000 synapses per neuron, and estimate the performance for much larger systems. Communication between neurons is identified as the computationally mostdemanding task and we present a novel method to overcome thisbottleneck. The simulator has already been used to study the primary visual system of the cat. 1 INTRODUCTION Neural networks have been implemented previously on massively parallel supercomputers (Fujimotoet al., 1992, Zhang et al., 1990). However, these are implementations ofartificial, highly simplified neural networks, while our aim was explicitly to provide a simulator for biologically realistic neural networks. There is also at least one implementation of biologically realistic neuronal systems on a moderately 904 Efficient Simulation of Biological Neural Networks 905 parallel but powerful machine (De Schutter and Bower, 1992), but the complexity of the used neuron model makes simulation of larger numbers of neurons impractical. Ourinterest here is to provide an efficient simulator of large neural networks of cortex and related subcortical structures. The most important characteristics of the neuronal systems we want to simulate are the following: - Cells are highly interconnected (several thousand connections per cell) but far from fully interconnected.

A Guided Tour of Computer Vision: A Review

Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp -- A Review

This program weaves together three threads: (1) AI, areas of AI.

Cambridge, If you would like us to consider your Mass.: The MIT Press, 1994.