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Working memory capacity of crows and monkeys arises from similar neuronal computations

Citation

Hahn, Lukas; Balakhonov, Dmitry; Rose, Jonas (2021), Working memory capacity of crows and monkeys arises from similar neuronal computations, Dryad, Dataset, https://doi.org/10.5061/dryad.0k6djhb1q

Abstract

Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (Corvus corone) in a change detection task, developed for monkeys (Macaca mulatta), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.

Methods

Data was collected from two carrion crows. Behavioral data was collected by Matlab. Electrophysiological data was collected by extracellular single neuron recordings using multi-channel microelectrodes and Intan RHD2000 headstages and USB-Interface board. Recordings were performed at a sampling rate of 30 kHz with a band-pass filter (0.5 kHz - 7.5 kHz). Spike sorting was performed using Klusta-suite software and spike times were imported into Matlab. Data were further processed and organized using custom Matlab code.

Usage Notes

Data and code have been compressed into a .zip folder each. Unpack the contents of the folders to use the dataset. The dataset is split into two main folders and one Matlab file:

'code'
contains all analysis code to produce all figures and reported statistics of the manuscript (refer to the
 MATLAB live script 'exeResultsLiveScript.m' to run the analysis, please adjust the path information of where the data is stored on your computer)

'ResultsStatistics.mat'
contains all reported statistical values (generated by 'exeResultsLiveScript.m')

'sourceData'
contains all required source data files (i.e. pre-processed data) required to run the analyses stored in
'code'

Funding

Volkswagen Foundation, Award: Freigeist Fellowship 93299

Deutsche Forschungsgemeinschaft, Award: Project B13 of the collaborative research center 874 (122679504)