Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.Defense Advanced Research Projects Research Agency (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC subcontract 801881-BS under prime contract HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (SBE-0354378

Adaptive Resonance Theory

Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Fuzzy ART Choice Functions

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

An ART1 microchip and its use in multi-ART1 systems

Recently, a real-time clustering microchip neural engine based on the ART1 architecture has been reported. Such chip is able to cluster 100-b patterns into up to 18 categories at a speed of 1.8 μs per pattern. However, that chip rendered an extremely high silicon area consumption of 1 cm2, and consequently an extremely low yield of 6%. Redundant circuit techniques can be introduced to improve yield performance at the cost of further increasing chip size. In this paper we present an improved ART1 chip prototype based on a different approach to implement the most area consuming circuit elements of the first prototype: an array of several thousand current sources which have to match within a precision of around 1%. Such achievement was possible after a careful transistor mismatch characterization of the fabrication process (ES2-1.0 μm CMOS). A new prototype chip has been fabricated which can cluster 50-b input patterns into up to ten categories. The chip has 15 times less area, shows a yield performance of 98%, and presents the same precision and speed than the previous prototype. Due to its higher robustness multichip systems are easily assembled. As a demonstration we show results of a two-chip ART1 system, and of an ARTMAP system made of two ART1 chips and an extra interfacing chip

Redistribution of Synaptic Efficacy Supports Stable Pattern Learning in Neural Networks

Markram and Tsodyks, by showing that the elevated synaptic efficacy observed with single-pulse LTP measurements disappears with higher-frequency test pulses, have critically challenged the conventional assumption that LTP reflects a general gain increase. Redistribution of synaptic efficacy (RSE) is here seen as the local realization of a global design principle in a neural network for pattern coding. As is typical of many coding systems, the network learns by dynamically balancing a pattern-independent increase in strength against a pattern-specific increase in selectivity. This computation is implemented by a monotonic long-term memory process which has a bidirectional effect on the postsynaptic potential via functionally complementary signal components. These frequency-dependent and frequency-independent components realize the balance between specific and nonspecific functions at each synapse. This synaptic balance suggests a functional purpose for RSE which, by dynamically bounding total memory change, implements a distributed coding scheme which is stable with fast as well as slow learning. Although RSE would seem to make it impossible to code high-frequency input features, a network preprocessing step called complement coding symmetrizes the input representation, which allows the system to encode high-frequency as well as low-frequency features in an input pattern. A possible physical model interprets the two synaptic signal components in terms of ligand-gated and voltage-gated receptors, where learning converts channels from one type to another.Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657

ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

Sensor Signal Analysis By Neural Networks For Surveillance In Nuclear Reactors

The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft [17] are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs. ART 2-A is selected for its speed. Two simulators are built. One is ART 2, and the other ART 2-A. The result is a success for both paradigms, and the study shows that ART 2-A is not only able to learn and distinguish the patterns from each other, its learning speed is also extremely fast despite the high-dimensional input spaces. © 1992 IEE