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Grammatical Bigrams
Grammatical Bigrams

... set of probabilistic phrase-structure rules , as in [6]; alternatively, the predependent and postdependent subsequences can be modeled separately by Markov chains that are specific to the head word, as in [8]. Consider a much stronger independence assumption: that all the dependents of a head word a ...
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... • Sensory receptors are structures in the skin and other tissues that detect changes in the internal or external environment. These receptors consist of specialized neuron endings or specialized cells in close contact with neurons that convert the energy of the stimulus (sound, color, odor, etc.) to ...
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... place) refers to the spatial arrangement of where sounds of different frequency are processed in the brain. Tones close to each other in terms of frequency are represented in topologically neighbouring regions in the brain.) ...
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... quantify the responses of neurons in the lateral geniculate nucleus to whitenoise and naturalistic movie stimuli. At the cellular level, spike-timingdependent plasticity operates at millisecond timescales; therefore, models seeking biological relevance should be able to perform at these temporal sca ...
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... age related macular degeneration and retinitis pigmentosa. Blindness results from loss of photoreceptors, but other retinal neurons maintain an active connection to the brain. We are developing a chronic retinal implant in hopes of restoring vision to these patients. The goal is to stimulate the rem ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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