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Memory in the brain isn't super-well understood, so going to the level of "data-structures" isn't really possible with purely biological models. Not that a purely biolgocial description would be very useful anyways. When people ask how the brain works, they typically don't want to be told "molecule A interacts with molecule B, which triggers molecule C". ...


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The brain structure for memory, association, learning and thinking works more like a network of weighted, linked nodes. in machine learning and related fields, artificial neural networks (ANNs) are computational models inspired by an animal's central nervous systems (in particular the brain) [...] Artificial neural networks are generally presented as ...


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If you want a very technical term, you could use "paraphasia", but most people just call it a "speech error" or "naming error". These are usually categorized according to error type: such as semantic (cat --> dog), phonological or "formal" (cat --> mat), mixed (both semantic and phonological: cat --> rat), nonword (cat --> kag), unrelated (cat --> log), etc. ...



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