Abstract / Description of output
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.
| Original language | English |
|---|---|
| Article number | P03007 |
| Number of pages | 13 |
| Journal | Journal of Statistical Mechanics: Theory and Experiment |
| Volume | 2013 |
| Issue number | 03 |
| DOIs | |
| Publication status | Published - 12 Mar 2013 |
Keywords / Materials (for Non-textual outputs)
- neuronal networks (theory)
- learning theory
- LONG-TERM POTENTIATION
- NEURAL-NETWORKS
- PROTEIN-SYNTHESIS
- PLASTICITY
- INDUCTION
- SYSTEMS
- EXPOSURE
- LTP
- RECONSOLIDATION
- CONSOLIDATION