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ShareScore release 0.9.0
Dataset results
2 results for “Sensorimotor transformation”
Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"
<p>Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Mayrhofer_Neuron.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Mayrhofer_data_code.zip" (~20 GB) is a zipped version of a folder "Mayrhofer_data_code" (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named "MatlabCode", and the specific code for generating each figure panel is in a sub-subfolder named "Figures_tjM1_paper". When running the code, you need to set the Matlab file path to be "Mayrhofer_data_code". In addition, you should add the folder "Mayrhofer_data_code" with subfolders in Matlab "Set Path". The figures will be saved in a subfolder named "Figures". Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.</p>
Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"
<p>Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831<br><br></p> <p>There are 2 files in this upload:</p> <p>1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon.</p> <p>2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".</p>
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