scholarly journals Dataset of active avoidance in Wistar-Kyoto and Sprague Dawley rats: Experimental data and reinforcement learning model code and output

Data in Brief ◽  
2020 ◽  
Vol 32 ◽  
pp. 106074
Author(s):  
John Palmieri ◽  
Kevin M. Spiegler ◽  
Kevin C.H. Pang ◽  
Catherine E. Myers
Keyword(s):  
Experimental Data ◽  
Reinforcement Learning ◽  
Active Avoidance ◽  
Learning Model ◽  
Sprague Dawley ◽  
Model Code ◽  
Sprague Dawley Rats ◽  
Wistar Kyoto ◽  
Reinforcement Learning Model

scholarly journals A reinforcement-learning model of active avoidance behavior: Differences between Sprague Dawley and Wistar-Kyoto rats

2020 ◽  
Vol 393 ◽  
pp. 112784 ◽  
Author(s):  
Kevin M. Spiegler ◽  
John Palmieri ◽  
Kevin C.H. Pang ◽  
Catherine E. Myers
Keyword(s):  
Reinforcement Learning ◽  
Avoidance Behavior ◽  
Active Avoidance ◽  
Learning Model ◽  
Sprague Dawley ◽  
Wistar Kyoto Rats ◽  
Wistar Kyoto ◽  
Reinforcement Learning Model ◽  
Active Avoidance Behavior

A Reinforcement Learning Model for Robots as Teachers*

2018 ◽  
Author(s):  
Sayanti Roy ◽  
Christopher Crick ◽  
Emily Kieson ◽  
Charles Abramson
Keyword(s):  
Reinforcement Learning ◽  
Learning Model ◽  
Reinforcement Learning Model

High-NaCl diets increase natriuretic and diuretic responses in salt-resistant but not salt-sensitive SHR

1991 ◽  
Vol 260 (6) ◽  
pp. F890-F897 ◽  
Author(s):  
M. S. Mozaffari ◽  
S. Jirakulsomchok ◽  
Z. H. Shao ◽  
J. M. Wyss
Keyword(s):  
Arterial Pressure ◽  
Renal Denervation ◽  
Isotonic Saline ◽  
Renal Nerves ◽  
Sprague Dawley ◽  
Volume Loading ◽  
Spontaneously Hypertensive ◽  
Volume Load ◽  
Sprague Dawley Rats ◽  
Wistar Kyoto

This study tested the hypothesis that NaCl-sensitive spontaneously hypertensive rats (SHR-S) display a defect in natriuretic and diuretic responses to acute volume loading that contributes to the rise in arterial pressure observed when the rats are fed a high-NaCl diet. Seven-week-old SHR-S and NaCl-resistant SHR rats (SHR-R) and normotensive (Wistar-Kyoto and Sprague-Dawley rats) were fed high- or basal NaCl diets. After 2.5 wk on the diets, preinstrumented conscious rats received an intravenous infusion (5% body wt; 0.5 ml/min) of isotonic saline, and urine was collected through a bladder catheter for 90 min. Control rats on the high-NaCl diet (compared with basal) excreted a significantly greater percentage of Na+ and volume load. In contrast, SHR-S on high-NaCl diet (compared with basal) had a very small increase in natriuretic response and no increase in diuretic response to volume expansion. The effect of renal denervation on natriuretic and diuretic responses to volume load was tested. In SHR-R on 1 and 8% NaCl diets, renal denervation had little or no effect on these responses, suggesting that renal nerves do not play a prominent role in the dietary NaCl-induced increases in the natriuretic and diuretic responses to volume load. These results demonstrate that NaCl-resistant rats rapidly adapt to diets high in NaCl content with increased natriuretic and diuretic responses to acute volume loading. The failure of SHR-S to adapt to the dietary challenge may result in volume loading and a secondary increase in arterial pressure after feeding.

scholarly journals Modeling individual variation in visual search with reinforcement learning

2020 ◽  
Author(s):  
Ben Lonnqvist ◽  
Micha Elsner ◽  
Amelia R. Hunt ◽  
Alasdair D F Clarke
Keyword(s):  
Visual Acuity ◽  
Reinforcement Learning ◽  
Visual Search ◽  
Individual Variation ◽  
Learning Model ◽  
Participant Preferences ◽  
Search Experiment ◽  
Reinforcement Learning Model ◽  
Specific Learning

Experiments on the efficiency of human search sometimes reveal large differences between individual participants. We argue that reward-driven task-specific learning may account for some of this variation. In a computational reinforcement learning model of this process, a wide variety of strategies emerge, despite all simulated participants having the same visual acuity. We conduct a visual search experiment, and replicate previous findings that participant preferences about where to search are highly varied, with a distribution comparable to the simulated results. Thus, task-specific learning is an under-explored mechanism by which large inter-participant differences can arise.

scholarly journals Ramping and phasic dopamine activity accounts for efficient cognitive resource allocation during reinforcement learning

2018 ◽  
Author(s):  
Minryung R. Song ◽  
Sang Wan Lee
Keyword(s):  
Experimental Data ◽  
Resource Allocation ◽  
Reinforcement Learning ◽  
Learning Model ◽  
Temporal Difference ◽  
Value Change ◽  
Cognitive Resource ◽  
A Value ◽  
Insight Into ◽  
And Task

AbstractDopamine activity may transition between two patterns: phasic responses to reward-predicting cues and ramping activity arising when an agent approaches the reward. However, when and why dopamine activity transitions between these modes is not understood. We hypothesize that the transition between ramping and phasic patterns reflects resource allocation which addresses the task dimensionality problem during reinforcement learning (RL). By parsimoniously modifying a standard temporal difference (TD) learning model to accommodate a mixed presentation of both experimental and environmental stimuli, we simulated dopamine transitions and compared it with experimental data from four different studies. The results suggested that dopamine transitions from ramping to phasic patterns as the agent narrows down candidate stimuli for the task; the opposite occurs when the agent needs to re-learn candidate stimuli due to a value change. These results lend insight into how dopamine deals with the tradeoff between cognitive resource and task dimensionality during RL.

scholarly journals Operant behavior controlled by position of a moving object – a reinforcement learning model

2008 ◽  
Vol 9 (S1) ◽  
Author(s):  
Cyril Brom ◽  
Daniel Klement ◽  
Michal Preuss
Keyword(s):  
Reinforcement Learning ◽  
Operant Behavior ◽  
Learning Model ◽  
Moving Object ◽  
Object A ◽  
Reinforcement Learning Model
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