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- Browsing Cognitive & Neural Systems by Issue Date
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Browsing Cognitive & Neural Systems by Issue Date
Now showing items 1-20 of 485
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-Integration-To-Endpoint ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders ...
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Synchronized Oscillations During Cooperative Feature Lining in a Cortical Model of Visual Perception(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)A neural network model of synchronized oscillations in visual cortex is presented to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these experiments, ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article introduces a quantitative model of early visual system function. The model is formulated to unify analyses of spatial and temporal information processing by the nervous system. Functional constraints of the ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)An improved Boundary Contour System (BCS) neural network model of preattentive vision is applied to two images that produce strong "pop-out" of emergent groupings in humans. In humans these images generate groupings collinear ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)Working memory neural networks are characterized which encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals. This temporal order ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article describes further evidence for a new neural network theory of biological motion perception that is called a Motion Boundary Contour System. This theory clarifies why parallel streams Vl-> V2 and Vl-> MT exist ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)The origin of the tri-phasic burst pattern, observed in the EMGs of opponent muscles during rapid self-terminated movements, has been controversial. Here we show by computer simulation that the pattern emerges from ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-04)This article describes further evidence for a new neural network theory of biological motion perception. The theory clarifies why parallel streams Vl --> V2, Vl --> MT, and Vl --> V2 --> MT exist for static form and motion ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-04)
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-04)Working memory neural networks are characterized which encode the invariant temporal order of sequential events. Inputs to the networks, called Sustained Temporal Order REcurrent (STORE) models, may be presented at widely ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-05)A neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy grayscale or multi-colored images. The figures can then ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-06)A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-06)A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-08)A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or ...
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(Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-08)A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, ...
Now showing items 1-20 of 485
