DOI: 10.5061/dryad.4j0zpc88b
title: Stable and dynamic representations of value in the prefrontal cortex

description: This data set was compiled for the experiments detailed in Enel, P.,
 Wallis, J., & Rich, E. (2019). Stable and dynamic representations
 of value in the prefrontal cortex. Elife 9 (2020): e54313
 10.7554/eLife.54313 Optimal decision-making requires that stimulus-value
 associations are kept up to date by constantly comparing the expected
 value of a stimulus with its experienced outcome. To do this, value
 information must be held in mind when a stimulus and outcome are separated
 in time. However, little is known about the neural mechanisms of working
 memory (WM) for value. Contradicting theories have suggested WM requires
 either persistent or transient neuronal activity, with stable or dynamic
 representations respectively. To test these hypotheses, we recorded
 neuronal activity in the orbitofrontal and anterior cingulate cortex of
 two monkeys performing a valuation task. We found that features of all
 hypotheses were simultaneously present in prefrontal activity, and no
 single hypothesis was exclusively supported. Instead, mixed dynamics
 supported robust, time invariant value representations while also encoding
 the information in a temporally specific manner. We suggest that this
 hybrid coding is a critical mechanism supporting flexible cognitive
 abilities.
description: Monkeys were first trained a value based decision making task in
 which a cue predicted a delayed reward. A detailed explanation of the task
 and first analysis of the data with decision trials can be found in
 <em>Rich, E. L., & Wallis, J. D. (2016). Decoding subjective
 decisions from orbitofrontal cortex. Nature Neuroscience, 19(7),
 973–980</em> 10.1038/nn.4320. This data set only contains the data of single cue in which the animal was looking at a reward predicting cue, performed an unrelated joystick reponse task during a delay, and received the reward associated with the cue. Monkeys were head fixed, while single electrodes were lowered in the OFC (areas 11 and 13) and the dorsal bank of the ACC sulcus (area 24). All well isolated neurons were recorded without discrimination. Neurons were filtered out if there firing rate over the session was lower than 1Hz. This data set contains both single and multi units without distinction. The firing rate was estimated either with gaussian or square window, and with different parameters: - type of smoothing: either gaussian or square - the size of the smoothing window: either 100ms or 50ms - the step between each bin: 25ms, 50ms or 200ms - the alignment of the bins: either center to the original timing or to the end of the bin. When centered to the end of the bin, only data before the timing of the bin is included in the bin. Pseudo-population data sets are generated with a specific seed to match the number of trials in each condition across monkey and recording session.
description: This data set contains the smoothed and binned firing rate of
 single and multi units of orbitofrontal and anterior cingulate cortex
 neurons of macaque monkeys performing a value based decision making task.
 Different methods were used to estimate the firing rate of neurons, aimed
 at specific analyses in <em>Enel, P., Wallis, J., & Rich, E.
 (2019). Stable and dynamic representations of value in the prefrontal
 cortex. </em><i>Elife</i> <em>9 (2020): e54313
 </em>10.7554/eLife.54313. The data set is saved in three python pickle files. Pseudo-randomly generated pseudo-population data sets can be obtained from these unit data set files with an accompanying python script. Enel, Pierre, Joni D. Wallis, and Erin L. Rich. "Stable and dynamic representations of value in the prefrontal cortex." <i>Elife</i> 9 (2020): e54313.   Each file contains the unit data set in nested python containers (python dictionary or list). The details of the data structure can be found in the ReadMe file. Pseudo-population data sets can be generated with the same seeds used in Enel et al. (2019) by using the generate_population_dataset.py script. A list of python package dependency can be found in the ReadMe file.