Abstract
A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.
Original language | English (US) |
---|---|
Article number | 57 |
Journal | Frontiers in Computational Neuroscience |
Volume | 8 |
Issue number | MAY |
DOIs | |
State | Published - May 28 2014 |
Keywords
- Delayed categorization
- Intrinsic plasticity
- Prefrontal cortex
- Reward-dependent learning
- STDP
- Synaptic scaling
- Working memory
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
- Cellular and Molecular Neuroscience