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Storage, retrieval, and decoding of distributed representations are implemented by efficient auto-associative memories; using distributed memories based on the idea of Hebb's cell assemblies additionally provides a natural tool for emergence of generalization hierarchies.
According to prominent computational neuroscience models, feature representations are implemented in the brain by population coding across neurons with tuned response properties (Pouget, Dayan & Zemel, 2000; Seung & Sompolinsky, 1993).
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The procedure of extracting LBP features for facial representations is implemented as follows: First, a face image is divided into several non-overlapping blocks.
This model is founded on two principles: (1) stimuli is represented internally by two representations with different degrees of complexity and accuracy, and (2) the matching of these representations is implemented by a distance function.
Donald Hebb proposed that memory traces and mental representations were implemented by connectivity within a distributed network of neurons, and that the selective re-ignition of this assembly would be tantamount to recognition and/or recall [65].
All problem representations were implemented in C# on Windows.
Partial representation is implemented for chromosome representation.
The visualization of data representation is implemented as the rotation buffers.
This representation was implemented as part of a larger automated compliance checking system, the Semantic Natural Language Processing-based Automated Compliance Checking (SNACC) system.
For the discretized model, a 3D (x,y,z) representation is implemented using gridblock sizes of 100 ft × 100 ft × 10 ft.
To handle the uncertainty in cost estimation model that cannot be addressed by traditional analytical methods, a fuzzy logic-based knowledge representation is implemented in the developed system.
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