Browsing Cognitive & Neural Systems by Title

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Browsing Cognitive & Neural Systems by Title

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  • A Scalable Model of Cerebellar Adaptive Timing and Sequencing: The Recurrent Slide and Latch (RSL) Model
    Rhodes, Bradley; Bullock, Daniel (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-09)
    From the dawn of modern neural network theory, the mammalian cerebellum has been a favored object of mathematical modeling studies. Early studies focused on the fan-out, convergence, thresholding, and learned weighting of ...
  • Searching the Sky with CONFIGR-STARS
    Carpenter, Gail A.; Ravindran, Arun K. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009)
    SyNAPSE program of the Defense Advanced Projects Research Agency (HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, a National Science Foundation Science of Learning Center ...
  • Segmentation ART: A Neural Network for Word Recognition from Continuous Speech
    Carpenter, Gail A.; Wilson, Frank D. M. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1998-05)
    The Segmentation ATIT (Adaptive Resonance Theory) network for word recognition from a continuous speech stream is introduced. An input sequeuce represents phonemes detected at a preproccesing stage. Segmentation ATIT is ...
  • Self-organization of Binocular Disparity Tuning by Reciprocal Corticogeniculate Interactions
    Grunewald, Alexander; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-10)
    This article develops a neural model of how sharp disparity tuning can arise through experience-dependent development of cortical complex cells. This learning process clarifies how complex cells can binocularly match left ...
  • Self-Organization of Topographic Mixture Networks Using Attentional Feedback
    Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-10)
    This paper proposes a biologically-motivated neural network model of supervised learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal ...
  • Self-Organizing Features and Categories
    Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-04)
    Because "people create features to subserve the representation and categorization of objects" (p. 3), the authors "provide an account of feature learning in which the components of a representation have close ties to the ...
  • Self-Organizing Grammar Induction Using a Neural Network Model
    Mannes, Christian (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-03)
    This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical ...
  • Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System
    Carpenter, Gail; Martens, Siegfried; Ogas, Ogi (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2004-01)
    Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals ...
  • Self-Organizing Hierarchical Knowledge Discovery by an Artmap Information Fusion System
    Carpenter, Gail; Martens, Siegfried (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2005-01)
    Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods ...
  • Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks
    Carpenter, Gail; Martens, Siegfried; Ogas, Ogi (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2004-12)
    Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and ...
  • A Self-Organizing Neural Model for Motor Equivalent Phoneme Production
    Guenther, Frank H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two ...
  • A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm
    Bullock, Daniel; Grossberg, Stephen; Guenther, Frank H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-12)
    This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and ...
  • A Self-Organizing Neural Network Architecture for Navigation Using Optic Flow
    Cameron, Seth; Grossberg, Stephen; Guenther, Frank H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1995-12)
    This paper describes a self-organizing neural network architecture that transforms optic now information into representations of heading, scene depth, and moving object locations. These representations are used to reactively ...
  • A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position
    Bullock, Daniel; Greve, Douglas; Grossberg, Stephen; Guenther, Frank H. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component ...
  • A Self-Organizing Neural Network Model for Redundant Sensory-Motor Control, Motor Equivalence, and Tool Use
    Bullock, Daniel; Grossberg, Stephen; Guenther, Frank (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-02)
    A neural network is introduced which provides a solution of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory ...
  • Self-Organizing Neural Networks for Spatial Planning and Flexible Arm Movement Control
    Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-08)
    This talk will survey recent results concerning how the brain self-organizes its planning and control of flexible arm movements to accomplish spatially defined tasks at variable speeds and forces with a redundant arm that ...
  • A Self-Organizing Neural System for Learning to Recognize Textured Scenes
    Grossberg, Stephen; Williamson, James (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1997-01)
    A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices ...
  • A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar
    Grossberg, Stephen; Williamson, James (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-01)
    A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image ...
  • Self-Supervised ARTMAP
    Amis, Gregory P.; Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-05)
    Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input ...
  • Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns
    Vogh, James (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have ...

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