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Cerebellum spiking data during simulated saccade control

Authors: Fruzzetti, Lorenzo; Kalidindi, Hari Teja; Antonietti, Alberto; Alessandro, Cristiano; Geminiani, Alice; Casellato, Claudia; Falotico, Egidio; +1 Authors

Cerebellum spiking data during simulated saccade control

Abstract

This repository contains data generated by simulating the cerebellum spiking neural network for saccade motor control. Please refer to the article at https://doi.org/10.1101/2022.03.14.483471. Please check the journal of PLOS computational biology for the published version of this article (inpress). The python scripts will generate plots from the simulation datasets. In order to do so, please unzip the datasets and put each of the data subfolders into the current folder along with the plotting scripts. The spiking neuronal circuit datasets have been generated using the NEST spiking neural network library (https://www.nest-simulator.org/), in .gdf format. Users can refer to the presented python scripts to understand how to read from and plot the data. Additionally, for interested users, the trained PF-PC synaptic weights from saccade simulations are also available as EXCEL files in the same dataset folders. In case of queries or any problems, please email me at the email id given in the article linked above.

Keywords

Cerebellum, Spiking neural network, optimal motor control, saccades

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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