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Computational modelling of memory retention from synapse to behaviour

Research output: Contribution to journalArticle

Original languageEnglish
Article numberP03007
Number of pages13
Journal Journal of Statistical Mechanics: Theory and Experiment
Volume2013
Issue number03
DOIs
Publication statusPublished - 12 Mar 2013

Abstract

One of our most intriguing mental abilities is the capacity to store information and recall it from memory. Computational neuroscience has been influential in developing models and concepts of learning and memory. In this tutorial review we focus on the interplay between learning and forgetting. We discuss recent advances in the computational description of the learning and forgetting processes on synaptic, neuronal, and systems levels, as well as recent data that open up new challenges for statistical physicists.

    Research areas

  • neuronal networks (theory), learning theory, LONG-TERM POTENTIATION, NEURAL-NETWORKS, PROTEIN-SYNTHESIS, PLASTICITY, INDUCTION, SYSTEMS, EXPOSURE, LTP, RECONSOLIDATION, CONSOLIDATION

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