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Browsing CAS/CNS Technical Reports by Title

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Browsing CAS/CNS Technical Reports by Title

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  • Chelian, Suhas; Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2004-10)
  • Bullock, Daniel; Contreras-Vidal, Jose L.; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes ...
  • Hampson, Michelle; Guenther, Frank H.; Cohen, Michael A.; Nieto-Castanon, Alfonso (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2003-02)
    To investigate the process underlying audiovisual speech perception, the McGurk illusion was examined across a range of phonetic contexts. Two major changes were found. First, the frequency of illusory /g/ fusion percepts ...
  • Hampson, Michelle; Guenther, Frank; Cohen, Michael (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-11)
    The McGurk effect has generally been studied within a limited range of phonetic contexts. With the goal of characterizing the McGurk effect through a wider range of contexts, a parametric investigation across three different ...
  • Martens, Siegfried (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2005-05)
  • Granger, Eric; Rubin, Mark; Grossberg, Stephen; Lavoie, Pierre (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-01)
    The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications ...
  • Versace, Massimiliano; Lorenz, Sean; St. Clair, Tim; Panko, Mikhail; Zaydens, Eugene; Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2008-06-16)
  • Pedini, David; Gaudiano, Paolo (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1994-08)
    The Adaptive Resonance Theory (ART) architecture, first proposed by (Grossberg, 1976b, 1976a), is a self-organizing neural network for stable pattern categorization in response to arbitrary input sequences. Since its ...
  • Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-11)
    In order to benefit from the advantages of localist coding, neural models that feature winner-take-all representations at the top level of a network hierarchy must still solve the computational problems inherent in distributed ...
  • Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-01)
  • Howe, Piers (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-09)
  • Carpenter, Gail A.; Grossberg, Stephen; Iizuka, Kunihiko (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-02)
    This article compares the performance of Fuzzy ARTMAP with that of Learned Vector Quantization and Back Propagation on a handwritten character recognition task. Training with Fuzzy ARTMAP to a fixed criterion used many ...
  • Granger, Eric; Grossberg, Stephen; Lavoie, Pierre; Rubin, Mark (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1999-05)
    ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform ...
  • Williamson, James R. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-05)
    Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of ...
  • Bullock, Daniel; Rhodes, Bradley (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2002-02)
  • Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1998-003)
  • Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-09)
    How do our brains so effectively achieve adaptive behavior in a changing world? Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within ...
  • Tal, Doron; Schwartz, Eric L. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1995-09)
    The integrate and fire model (Stein, 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold and refractory period. Integrate and fire (IAF) neurons ...
  • Carpenter, Gail A.; Gaddam, Chaitanya Sai; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-09)
    CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial ...
  • Lehar, Steven (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1993-01)
    The Boundary Contour System neural vision model reproduces perceptual illusory boundary formation by a conjunctive boundary completion process within a large cellular receptive field. The conjunctive chain allows the same ...

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