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Taxonomical associative memory


Abstract Assigning categories to objects allows the mind to code experience by concepts, thus easing the burden in perceptual, storage, and reasoning processes. Moreover, maximal efficiency of cognitive resources is attained with categories that best mirror the structure of the perceived world. In this work, we will explore how taxonomies could be represented in the brain, and their application in learning and recall. In a recent work, Sacramento and Wichert (in Neural Netw 24(2):143–147, 2011) proposed a hierarchical arrangement of compressed associative networks, improving retrieval time by allowing irrelevant neurons to be pruned early. We present an extension to this model where superordinate concepts are encoded in these compressed networks. Memory traces are stored in an uncompressed network, and each additional network codes for a taxonomical rank. Retrieval is progressive, presenting increasingly specific superordinate concepts. The semantic and technical aspects of the model are investigated in two studies: wine classification and random correlated data.
Year 2013
Keywords Neural Computation;
Authors Diogo Rendeiro, João Sacramento, Andreas Wichert
Journal Cognitive Computation
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@article { rendeiro13, abstract = {Assigning categories to objects allows the mind to code experience by concepts, thus easing the burden in perceptual, storage, and reasoning processes. Moreover, maximal efficiency of cognitive resources is attained with categories that best mirror the structure of the perceived world. In this work, we will explore how taxonomies could be represented in the brain, and their application in learning and recall. In a recent work, Sacramento and Wichert (in Neural Netw 24(2):143–147, 2011) proposed a hierarchical arrangement of compressed associative networks, improving retrieval time by allowing irrelevant neurons to be pruned early. We present an extension to this model where superordinate concepts are encoded in these compressed networks. Memory traces are stored in an uncompressed network, and each additional network codes for a taxonomical rank. Retrieval is progressive, presenting increasingly specific superordinate concepts. The semantic and technical aspects of the model are investigated in two studies: wine classification and random correlated data.}, journal = {Cognitive Computation}, keywords = {Neural Computation;}, title = {Taxonomical associative memory}, year = {2013}, author = {Diogo Rendeiro and João Sacramento and Andreas Wichert} }

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