The advantages of biology for design and testing of AI systems include large amounts of available online data, significant (but incomplete) background knowledge, a wide variety of problems commensurate with AI technologies, clear standards of success, cooperative domain experts, non-military basic research support and percieved potential for practical (and profitable) applications. These considerations have motivated a growing group of researchers to pursue both basic and applied AI work in the domain. More than seventy-five researchers working on these problems gathered at Stanford for a AAAI sponsored symposium on the topic. This article provides a description of much of the work presented at the meeting, and fills in the basic biology background necessary to place it in context.

We present an application of back-propagation networks to handwritten digitrecognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service. 1 INTRODUCTION The main point of this paper is to show that large back-propagation (BP) networks canbe applied to real image-recognition problems without a large, complex preprocessing stage requiring detailed engineering. Unlike most previous work on the subject (Denker et al., 1989), the learning network is directly fed with images, rather than feature vectors, thus demonstrating the ability of BP networks to deal with large amounts of low level information. Previous work performed on simple digit images (Le Cun, 1989) showed that the architecture of the network strongly influences the network's generalization ability. Good generalization can only be obtained by designing a network architecture that contains a certain amount of a priori knowledge about the problem. The basic design principleis to minimize the number of free parameters that must be determined by the learning algorithm, without overly reducing the computational power of the network.

To achieve high-rate image data compression while maintainig a high quality reconstructed image, a good image model and an efficient way to represent the specific data of each image must be introduced. Based on the physiological knowledge of multi - channel characteristics and inhibitory interactions between them in the human visual system, a mathematically coherent parallel architecture for image data compression which utilizes the Markov random field Image model and interactions between a vast number of filter banks, is proposed.

Gale L. Martin James A. Pittman MCC, Austin, Texas 78759 ABSTRACT We are developing a hand-printed character recognition system using a multilayered neuralnet trained through backpropagation. We report on results of training nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a stylus digitizer.Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing practical pattern recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. Reducing capacity does have other benefits however, especially when the reduction isaccomplished by using local receptive fields with shared weights. In this latter case, we find the net evolves feature detectors resembling those in visual cortex and Linsker's orientation-selective nodes.

This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and compares its performance with Back Propagation in a signal separation application. The problem is to separate two signals; a modem data signal and a male speech signal, added and transmitted through a 4 khz channel. The signals are sampled at 8 khz, and using supervised learning, an attempt is made to reconstruct them. The SONN is an algorithm that constructs its own network topology during training, which is shown to be much smaller than the BP network, faster to trained, and free from the trial-anderror network design that characterize BP. 1. INTRODUCTION The research in Neural Networks has witnessed major changes in algorithm design focus, motivated by the limitations perceived in the algorithms available at the time.

The properties of a cluster of multiple back-propagation (BP) networks are examined and compared to the performance of a single BP network. Theunderlying idea is that a synergistic effect within the cluster improves the perfonnance and fault tolerance. Five networks were initially trainedto perfonn the same input-output mapping. Following training, a cluster was created by computing an average of the outputs generated by the individual networks. The output of the cluster can be used as the desired output during training by feeding it back to the individual networks.In comparison to a single BP network, a cluster of multiple BP's generalization and significant fault tolerance. It appear that cluster advantage follows from simple maxim "you can fool some of the single BP's in a cluster all of the time but you cannot fool all of them all of the time" {Lincoln} 1 INTRODUCTION Shortcomings of back-propagation (BP) in supervised learning has been well documented inthe past {Soulie, 1987; Bernasconi, 1987}. Often, a network of a finite size does not learn a particular mapping completely or it generalizes poorly.

Purdue University Purdue University Purdue University W. Lafayette, IN. 47907 W. Lafayette, IN. 47907 W. Lafayette, IN. 47907 ABSTRACT A nonlinear neural framework, called the Generalized Hopfield network, is proposed, which is able to solve in a parallel distributed manner systems of nonlinear equations. The method is applied to the general nonlinear optimization problem. We demonstrate GHNs implementing the three most important optimization algorithms, namely the Augmented Lagrangian, Generalized Reduced Gradient and Successive Quadratic Programming methods. The study results in a dynamic view of the optimization problem and offers a straightforward model for the parallelization of the optimization computations, thus significantly extending the practical limits of problems that can be formulated as an optimization problem and which can gain from the introduction of nonlinearities in their structure (eg. The ability of networks of highly interconnected simple nonlinear analog processors (neurons) to solve complicated optimization problems was demonstrated in a series of papers by Hopfield and Tank (Hopfield, 1984), (Tank, 1986).

In recent years many researchers have investigated the use of Markov Random Fields (MRFs) for computer vision. They can be applied for example to reconstruct surfaces from sparse and noisy depth data coming from the output of a visual process, or to integrate early vision processes to label physical discontinuities. In this paper weshow that by applying mean field theory to those MRFs models a class of neural networks is obtained. Those networks can speed up the solution for the MRFs models. The method is not restricted to computer vision. 1 Introduction

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Truth maintenance is a collection of techniques for doing belief revision. A truth maintenance system's task is to maintain a set of beliefs in such a way that they are not known to be contradictory and no belief is kept without a reason. Truth maintenance systems were introduced in the late seventies by Jon Doyle and in the last five years there has been an explosion of interest in this kind of systems. In this paper we present an annotated bibliography to the literature of truth maintenance systems, grouping the works referenced according to several classifications.