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

John Palmieri; Kevin M Spiegler; Kevin C H Pang; Catherine E Myers

doi:10.1016/j.dib.2020.106074

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

Data Brief. 2020 Jul 25:32:106074. doi: 10.1016/j.dib.2020.106074. eCollection 2020 Oct.

Authors

John Palmieri^{1

2}, Kevin M Spiegler^{1

2}, Kevin C H Pang^{2

3

4}, Catherine E Myers^{2

3

4}

Affiliations

¹ Rutgers New Jersey Medical School, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ 07103, USA.
² Rutgers School of Graduate Studies, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ 07103, USA.
³ Department of Veterans Affairs, New Jersey VA Health Care System, 385 Tremont Avenue, East Orange, NJ 07018, USA.
⁴ Department of Pharmacology, Physiology, and Neuroscience, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ 07103, USA.

Abstract

Data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during an active escape-avoidance experiment. Footshock could be avoided by pressing a lever during a danger period prior to onset of shock. If avoidance did not occur, a series of footshocks was administered, and the rat could press a lever to escape (terminate shocks). For each animal, data were simplified to the presence or absence of lever press and stimuli in each 12-second time frame. Using the pre-processed dataset, a reinforcement learning (RL) model, based on an actor-critic architecture, was utilized to estimate several different model parameters that best characterized each rat's behaviour during the experiment. Once individual model parameters were determined for all 80 rats, behavioural recovery simulations were run using the RL model with each animal's "best-fit" parameters; the simulated behaviour generated avoidance data (percent of trials avoided during a given experimental session) that could be compared across simulated rats, as is customarily done with empirical data. The datasets representing both the experimental data and the model-generated data can be interpreted in various ways to gain further insight into rat behaviour during avoidance and escape learning. Furthermore, the estimated parameters for each individual rat can be compared across groups. Thus, possible between-strain differences in model parameters can be detected, which might provide insights into strain differences in learning. The software implementing the RL model can also be applied to or serve as a template for other experiments involving acquisition learning. Reference for Co-Submission: K.M. Spiegler, J. Palmieri, K.C.H. Pang, C.E. Myers, A reinforcement-learning model of active avoidance behavior: Differences between Sprague-Dawley and Wistar-Kyoto rats. Behav. Brain Res. (2020 Jun 22[epub ahead of print]) doi: 10.1016/j.bbr.2020.112784.

Keywords: Avoidance learning; Computational biology; Computational modelling; Neurosciences; Reinforcement learning; Strain differences; Wistar Kyoto rat.

Abstract

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