Neural Basis of Semantic Memory by John Hart download in pdf, ePub, iPad
Semantic networks generally do not employ distributed representations for concepts, as may be found in a neural network. The links may be weighted such that some are stronger than others or, equivalently, have a length such that some links take longer to traverse than others.
Indeed, neural networks and semantic networks may be characterized as associative models of cognition. The distinction between semantic and episodic memory has become a part of the broader scientific discourse.
It holds generic information that is more than likely acquired across various contexts and is used across different situations. The first was that basic perceptrons were incapable of processing the exclusive-or circuit. For example, subjects might verify a sentence by comparing the feature sets that represent its subject and predicate concepts. Researchers started applying these ideas to computational models in with Turing's B-type machines. With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time.
This evolved into models for long term potentiation. Thus, items that co-occur more often are more strongly associated.
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