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5 results for “reward-based learning”

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zenodo44/100

Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"

<p>Data set for: Le Merre P, Esmaeili V, Charri&egrave;re E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1.&nbsp;&nbsp; &nbsp;&#39;2018_LeMerre_Neuron.pdf&#39; - this is a pdf version of the online publication.<br> 2.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_data.mat&#39; - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_data.mat&#39; - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4.&nbsp;&nbsp; &nbsp;&#39;Opto_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5.&nbsp;&nbsp; &nbsp;&#39;Mus_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6.&nbsp;&nbsp; &nbsp;&#39;Learning_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7.&nbsp;&nbsp; &nbsp;&#39;Exposed_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&rsquo;.<br> 9.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap2.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&rsquo;; &rsquo;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 10.&nbsp;&nbsp; &nbsp;&#39;scatterplot_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39;.<br> 11.&nbsp;&nbsp; &nbsp;&#39;SEP_colormtrx.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39;; &#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39;.<br> 12.&nbsp;&nbsp; &nbsp;&#39;zscore_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 13.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Chronic_LFP_dataViewer.m&#39;.<br> 14.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Chronic_LFP_data.mat&#39;.<br> 15.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Silicon_Probe_dataViewer.m&#39;.<br> 16.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Silicon_Probe_data.mat&#39;.<br> 17.&nbsp;&nbsp; &nbsp;&#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18.&nbsp;&nbsp; &nbsp;&#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19.&nbsp;&nbsp; &nbsp;&#39;plot_fig2A_SEP_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20.&nbsp;&nbsp; &nbsp;&#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21.&nbsp;&nbsp; &nbsp;&#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22.&nbsp;&nbsp; &nbsp;&#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23.&nbsp;&nbsp; &nbsp;&#39;plot_fig3B_Amplitude_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Randomization.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26.&nbsp;&nbsp; &nbsp;&#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27.&nbsp;&nbsp; &nbsp;&#39;plot_fig4A_SEP_H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28.&nbsp;&nbsp; &nbsp;&#39;plot_fig4B_Amplitude_ H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31.&nbsp;&nbsp; &nbsp;&#39;plot_fig4D_Photoinhibitions.m&#39; - this is a Matlab code, which analyses the data in &#39;Opto_Inactivation_data.mat&#39;, and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32.&nbsp;&nbsp; &nbsp;&#39;plot_figS2D_Performance_DetectionTask_NeutralExposition.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33.&nbsp;&nbsp; &nbsp;&#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34.&nbsp;&nbsp; &nbsp;&#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35.&nbsp;&nbsp; &nbsp;&#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36.&nbsp;&nbsp; &nbsp;&#39;plot_figS4B_Pharmacological_Inactivations.m&#39; - this is a Matlab code, which analyses the data in &#39;Mus_Inactivation_data.mat&#39;, and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37.&nbsp;&nbsp; &nbsp;&#39;Load_LFP_Multisite_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Chronic_LFP_data.mat&#39;.<br> 38.&nbsp;&nbsp; &nbsp;&#39;Load_Silicon_Probe_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Silicon_Probe_data.mat&#39;.<br> 39.&nbsp;&nbsp; &nbsp;&#39;Load_Optogenetic_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Opto_Inactivation_data.mat&#39;.<br> 40.&nbsp;&nbsp; &nbsp;&#39;Load_Pharmacological_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Mus_Inactivation_data.mat&#39;.<br> 41.&nbsp;&nbsp; &nbsp;&#39;bonf_holm.m&#39; - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code &#39;plot_figS4B_Pharmacological_Inactivations.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42.&nbsp;&nbsp; &nbsp;&#39;boundedline.m&#39; - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&rsquo;; &#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43.&nbsp;&nbsp; &nbsp;&#39;inpaint_nans.m&#39; - this is a Matlab code, which is called in the Matlab code &#39;boundedline.m&#39;.<br> 44.&nbsp;&nbsp; &nbsp;&#39;PSTH_Simple.m&#39; - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.</p>

opencc-by-4.0Dec 2017View details →
zenodo44/100

Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"

<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named &quot;<strong>2021_Sippy_FUNCTION.pdf</strong>&quot; is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named &quot;<strong>Sippy_data_code.zip</strong>&quot; (~5 GB) is a zipped version of a folder &lsquo;<em>Sippy_data_code</em>&rsquo;, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (&lsquo;<em>Sippy_data_code</em>&rsquo;). You first need to run &lsquo;AnalyzeDataStructure.m&rsquo; and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder &lsquo;Figures&rsquo;.</p> <p>The subfolder &lsquo;<em>Data</em>&rsquo; contains the data structure &lsquo;<em>Data.mat</em>&rsquo; to be analyzed, as well as a Matlab file called &lsquo;<em>p_value_colormap.mat</em>&rsquo; used to plot the p value color bars in some figures.</p> <p>The subfolder &lsquo;<em>Functions</em>&rsquo; contains functions called by the main codes.</p> <p>The subfolder &lsquo;<em>Codes</em>&rsquo; contains the following codes:</p> <p><em>&lsquo;AnalyzeDataStructure.m&rsquo;: </em>computes the results and saves them as a new data structure called &lsquo;<em>Analyzed_Data</em>&rsquo;, in the subfolder &lsquo;<em>Results</em>&rsquo;.</p> <p><em>&lsquo;Figure_1.m&rsquo;: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>&lsquo;Figure_2.m&rsquo;: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>&lsquo;Figure_3.m&rsquo;: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>&lsquo;SuppFigure_2.m&rsquo;: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>&lsquo;SuppFigure_3.m&rsquo;: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>&lsquo;SuppFigure_4.m&#39;: </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p>&nbsp;</p> <p>The data structures contain the following fields:</p> <p><em>&lsquo;Mouse_Name&rsquo;</em>: name of the mouse.</p> <p><em>&lsquo;Mouse_RecordingDate&rsquo;</em>: date of recording (YMD).</p> <p><em>&lsquo;Mouse_DateOfBirth&rsquo;</em>: date of birth of the mouse (YMD).</p> <p><em>&lsquo;Mouse_Sex&rsquo;</em>: sex of the mouse (F or M).</p> <p><em>&lsquo;Mouse_Genotype&rsquo;</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>&lsquo;Mouse_Level&rsquo;</em>: Training level (NA&Iuml;VE or EXPERT).</p> <p><em>&lsquo;Cell_Counter&rsquo;</em>: cell recorded in a given mouse.</p> <p><em>&lsquo;Cell_Type&rsquo;</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>&lsquo;Cell_TargetedBrainArea&rsquo;</em>: Brain area targeted (DLS).</p> <p><em>&lsquo;Cell_Recovered&rsquo;</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>&lsquo;Cell_Coordinates&rsquo;</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>&lsquo;Cell_Fluorescence&rsquo;</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>&lsquo;Sweep_Counter&rsquo;</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>&lsquo;Sweep_Type&rsquo;</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>&lsquo;Sweep_MembranePotential&rsquo;</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>&lsquo;Sweep_CurrentInjected&rsquo;</em>: current injected into the cell (pA).</p> <p><em>&lsquo;Sweep_PiezoLick&rsquo;</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>&lsquo;Sweep_Trial&rsquo;</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>&lsquo;Sweep_WhiskerStim&rsquo;</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>&lsquo;Sweep_Valve&rsquo;</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>&lsquo;Sweep_SamplingRate&rsquo;</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>&lsquo;Sweep_TimeStamp&rsquo;</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>&lsquo;Sweep_Reward&rsquo;</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>&lsquo;Sweep_APThresh&rsquo;</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2021View details →
zenodo40/100

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>

opencc-by-4.0Sep 2024View details →
zenodo36/100

Data set for "Neural mechanisms underlying uninstructed orofacial movements during reward-based learning tasks"

<p>Data set for&nbsp;Li, Nakano, et al. (2023) Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors. Current Biology,&nbsp;https://doi.org/10.1016/j.cub.2023.07.013</p> <p><br> The file &quot;Li_Nakano_et_al_detaset.zip&quot;&nbsp;(~45.4 GB)&nbsp;is a compressed version of the &quot;Li_Nakano_et_al_detaset&quot; folder. The uncompressed folder (~76.4 GB)&nbsp;houses the data and some analytical codes used in the study. Upon extraction, the folder reveals two subfolders,&nbsp;&quot;Raw data&quot; and &quot;Source data,&quot; and a &#39;README&#39; text file providing further instructions or information.</p>

opencc-by-4.0Aug 2023View details →
dryad36/100

Reconfigurations of cortical manifold structure during reward-based motor learning

Open the record for dataset details and reuse information.

publicJun 2024View details →

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