Name
Abstract | ||
|---|---|---|
Stress and genetic background regulate different aspects of behavioral learning through the action of stress hormones and neuromodulators. In reinforcement learning (RL) models, meta-parameters such as learning rate, future reward dis- count factor, and exploitation-exploration factor, contr ol learning dynamics and performance. They are hypothesized to be related to neuromodulatory levels in the brain. We found that many aspects of animal learning and performance can be described by simple RL models using dynamic control of the meta-parameters . To study the effects of stress and genotype, we carried out 5-hole-box light condition- ing and Morris water maze experiments with C57BL/6 and DBA/2mouse strains. The animals were exposed to different kinds of stress to evaluate its effects on immediate performance as well as on long-term memory. Then, we used RL mod- els to simulate their behavior. For each experimental sessi on, we estimated a set of model meta-parameters that produced the best fit between t he model and the animal performance. The dynamics of several estimated meta-parameters were qualitatively similar for the two simulated experiments, a nd with statistically sig- nificant differences between different genetic strains and stress conditions. |
Year | Venue | Keywords |
|---|---|---|
2006 | NIPS | morris water maze,genetics,simulation experiment,long term memory,reinforcement learning |
Field | DocType | Citations |
Morris water navigation task,Computer science,Stress conditions,Conditioning,Learning dynamics,Artificial intelligence,Machine learning,Reinforcement learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gediminas Luksys | 1 | 0 | 0.68 |
| Jérémie Knüsel | 2 | 0 | 0.34 |
| Denis Sheynikhovich | 3 | 74 | 6.77 |
| Carmen Sandi | 4 | 1 | 1.36 |
| Wulfram Gerstner | 5 | 2437 | 410.08 |