A mathematical model based on the bulbar anatomy and electrophysiology is described. Simulations produce a 35-60 Hz modulated activity coherent across the bulb, mimicing the observed field potentials. The decision states (for the odor information) here can be thought of as stable cycles, rather than point stable states typical of simpler neuro-computing models. Analysis and simulations show that a group of coupled nonlinear oscillators are responsible for the oscillatory activities determined by the odor input, andthat the bulb, with appropriate inputs from higher centers, can enhance or suppress the sensitivity to partiCUlar odors. The model provides a framework in which to understand the transform between odor input and the bulbar output to olfactory cortex.

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner,using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

This paper introduces a means to handle the critical problem of nonlocal role-bindingsin localist spreading-activation networks. Every conceptual node in the network broadcasts a stable, uniquely-identifying activation pattern, called its signature. A dynamic role-binding is created whena role's binding node has an activation that matches the bound concept's signature. Most importantly, signatures are propagated across long paths of nodes to handle the non-local role-bindings necessary forinferencing. Our localist network model, ROBIN (ROle Binding and Inferencing Network), uses signature activations to robustly representschemata role-bindings and thus perfonn the inferencing, plan/goal analysis, schema instantiation, word-sense disambiguation, anddynamic reinterpretation portions of the natural language understanding process.

Thishas resulted in t.he building of systems with semantically nameable nodes which perform inferencing by examining t.he pat,t.erns of activation spread.

The algorithm can automatically findoptimal or nearly optimal architectures necessary to solve known Boolean functions, facilitate the interpretation of the activation of the remaining hidden units and automatically estimate the complexity of architectures appropriate for phonetic labeling problems. The general principle of the algorithm can also be adapted to different tasks: for example, it can be used to eliminate the [0, 0] local minimum of the [-1.

This paper is concerced with the use of error back-propagation in phonetic classification. Our objective is to investigate the basic characteristicsof back-propagation, and study how the framework ofmulti-layer perceptrons can be exploited in phonetic recognition.

Phillip Alvelda, A. Miguel San Martin The Jet Propulsion Laboratory, California Institute of Technology, Pasadena, Ca. 91109 ABSTRACT Currently, the most complex spacecraft attitude determination and control tasks are ultimately governed by ground-based systems and personnel. Conventional on-board systems face severe computational bottlenecks introduced by serial microprocessors operating on inherently parallel problems. New computer architectures based on the anatomy of the human brain seem to promise high speed and fault-tolerant solutions to the limitations of serial processing. INTRODUCTION By design, a conventional on-board microprocessor can perform only one comparison or calculation at a time. Image or pattern recognition problems involving large template sets and high resolution can require an astronomical number of comparisons to a given database.

McGill University Montreal, Canada H3A2A7 PieroCosi Centro di Studio per Ie Ricerche di Fonetica, C.N.R., Via Oberdan,10, 35122 Padova, Italy ABSTRACT Preliminary results on speaker-independant speech recognition are reported. A method that combines expertise on neural networks with expertise on speech recognition is used to build the recognition systems. For transient sounds, eventdriven propertyextractors with variable resolution in the time and frequency domains are used. For sonorant speech, a model of the human auditory system is preferred to FFT as a front-end module. INTRODUCTION Combining a structural or knowledge-based approach for describing speech units with neural networks capable of automatically learning relations between acoustic properties and speech units is the research effort we are attempting.

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We consider a 2-layer, 3-node, n-input neural network whose nodes compute linear threshold functions of their inputs. We show that it is NPcomplete to decide whether there exist weights and thresholds for the three nodes of this network so that it will produce output consistent witha given set of training examples. We extend the result to other simple networks. This result suggests that those looking for perfect training algorithms cannot escape inherent computational difficulties just by considering only simple or very regular networks. It also suggests the importance, given a training problem, of finding an appropriate network and input encoding for that problem. It is left as an open problem to extend our result to nodes with nonlinear functions such as sigmoids.