Browsing CAS/CNS Technical Reports by Title

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Browsing CAS/CNS Technical Reports by Title

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  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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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