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18 results for “sensory drive”
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, Charrière 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. '2018_LeMerre_Neuron.pdf' - this is a pdf version of the online publication.<br> 2. 'Chronic_LFP_data.mat' - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3. 'Silicon_Probe_data.mat' - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4. 'Opto_Inactivation_data.mat' - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5. 'Mus_Inactivation_data.mat' - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6. 'Learning_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7. 'Exposed_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8. 'p_value_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig2B_Amplitude_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m'; 'plot_fig4A_SEP_H_vs_M.m’.<br> 9. 'p_value_colormap2.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m’; ’plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 10. 'scatterplot_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m'.<br> 11. 'SEP_colormtrx.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig1B_Sensory_Evoked_Potentials.m'; 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m'.<br> 12. 'zscore_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 13. 'Chronic_LFP_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Chronic_LFP_dataViewer.m'.<br> 14. 'Chronic_LFP_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Chronic_LFP_data.mat'.<br> 15. 'Silicon_Probe_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Silicon_Probe_dataViewer.m'.<br> 16. 'Silicon_Probe_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Silicon_Probe_data.mat'.<br> 17. 'plot_fig1B_Sensory_Evoked_Potentials.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18. 'plot_fig1C_Silicon_Probe_Hit_trials.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19. 'plot_fig2A_SEP_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20. 'plot_fig2B_Amplitude_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21. 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22. 'plot_fig3A_SEP_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23. 'plot_fig3B_Amplitude_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24. 'plot_fig3C_ROC_Trained_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25. 'plot_fig3C_ROC_Randomization.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26. 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27. 'plot_fig4A_SEP_H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28. 'plot_fig4B_Amplitude_ H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29. 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30. 'plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31. 'plot_fig4D_Photoinhibitions.m' - this is a Matlab code, which analyses the data in 'Opto_Inactivation_data.mat', and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32. 'plot_figS2D_Performance_DetectionTask_NeutralExposition.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33. 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34. 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35. 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36. 'plot_figS4B_Pharmacological_Inactivations.m' - this is a Matlab code, which analyses the data in 'Mus_Inactivation_data.mat', and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37. 'Load_LFP_Multisite_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Chronic_LFP_data.mat'.<br> 38. 'Load_Silicon_Probe_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Silicon_Probe_data.mat'.<br> 39. 'Load_Optogenetic_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Opto_Inactivation_data.mat'.<br> 40. 'Load_Pharmacological_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Mus_Inactivation_data.mat'.<br> 41. 'bonf_holm.m' - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code 'plot_figS4B_Pharmacological_Inactivations.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42. 'boundedline.m' - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m’; 'plot_fig3C_ROC_Trained_vs_Exposed.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4A_SEP_H_vs_M.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43. 'inpaint_nans.m' - this is a Matlab code, which is called in the Matlab code 'boundedline.m'.<br> 44. 'PSTH_Simple.m' - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.</p>
An early surge of norepinephrine along brainstem pathways drives sensory-evoked awakening
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Data from: Differential survival between visual environments supports a role of divergent sensory drive in cichlid fish speciation
Identifying the selective forces that initiate ecological speciation is a major challenge in evolutionary biology. Sensory drive has been implicated in speciation in various taxa, largely based on phenotype-environment correlations and signatures of selection in sensory genes. Here, we present a reciprocal transplant experiment revealing species differences in performance in alternative visual environments, consistent with speciation by divergent sensory drive. The closely related cichlids Pundamilia pundamilia and P. nyererei inhabit different visual environments in Lake Victoria and show associated differences in visual system properties. Mimicking the two light environments in the laboratory, we find a substantial reduction in survival of both species when reared in the other species' visual environment. This implies that the observed differences in Pundamilia colour vision are indeed adaptive, and substantiates the implicit assumption in sensory drive speciation models that divergent environmental selection is strong enough to drive divergence in sensory properties.
Data from: The pervasive effects of lighting environments on sensory drive in bluefin killifish: an investigation into male/male competition, female choice, and predation
Sensory drive predicts that the conditions under which signaling takes place have large effects on signals, sensory systems, and behavior. The coupling of an ecological genetics approach with sensory drive has been fruitful. An ecological genetics approach compares populations that experience different environments and asks whether population differences are adaptive and are the result of genetic and/or environmental variation. The multi-faceted effects of signaling environments are well-exemplified by the bluefin killifish. In this system, males with blue anal fins are abundant in tannin-stained swamps that lack UV/blue light but are absent in clear springs where UV/blue light is abundant. Past work indicates that lighting environments shape genetic and environmental variation in color patterns, visual systems, and behavior. Less is known about the selective forces creating the across population correlations between UV/blue light and the abundance of blue males. Here, we present three new experiments that investigate the roles of lighting environments on male competition, female mate choice, and predation. We found strong effects of lighting environments on male competition where blue males were more likely to emerge as dominant in tea-stained water than in clear water. Our preliminary study on predation indicated that blue males may be less susceptible to predation in tea-stained water than in clear water. However, there was little evidence for female preferences favoring blue males. The resulting pattern is one where the effects of lighting environments on genetic variation and phenotypic plasticity match the direction of selection and favor the expression of blue males in swamps.
Can sensory drive explain the evolution of visual signal diversity in terrestrial species? A test with Anolis lizards
<p>Animal signal colors evolve to efficiently stimulate conspecific visual systems. The sensory drive hypothesis proposes that species differences in habitat light conditions favor the evolution of color diversity. The strongest support comes from aquatic systems, while terrestrial systems offer fewer convincing examples. Anolis lizards occupy diverse habitats and signal with a colorful dewlap. Dewlap visibility depends on perceived chromatic contrast with the background. Visual-system modeling has shown that red dewlaps are most visible in most habitat types. However, a majority of species possess white or yellow dewlaps. In a recent behavioral study we showed that low light conditions can sometimes make yellow and white colors more visible, by altering chromatic contrast perception with the background. Using 17 Caribbean Anolis species we showed that cut-on wavelength, a measure of dewlap color in a white-to-red continuum, correlates with habitat light intensity. Pairwise comparisons revealed that red dewlaps are most visible in bright habitats, whereas yellow and white are more visible in darker habitats. We conclude that sensory drive has contributed to the evolution of dewlap color differences through the interactive effects of total habitat light intensity and chromatic contrast perception and may provide a mechanism for speciation among anoles.</p>
Data for: A test of Sensory Drive in plant-pollinator interactions: habitat heterogeneity shapes pollinator preference for a floral visual signal
<p>DATA:</p> <p>FinnKoski_PollData_Final: Pollinator visitation data to floral arrays analyzed</p> <p>Spectra used for vismodels.zip: reflectance spectra of flowers and floral backgrounds, irradiance spectra</p> <p>ColorContrastData: visual contrast data analyzed</p> <p>CODE: </p> <p>vismod_code.R : code used for visual system modeling and calculation of contrast</p> <p>SAS_modelcode.R: SAS code used to analyze pollinator visitation data and color contrast data</p>
Data from: The pervasive effects of lighting environments on sensory drive in bluefin killifish: an investigation into male/male competition, female choice, and predation
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Can sensory drive explain the evolution of visual signal diversity in terrestrial species? A test with Anolis lizards
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Data from: Sensory drive does not explain reproductive character displacement of male acoustic signals in the Upland Chorus Frog (Pseudacris feriarum)
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Data from: Differential survival between visual environments supports a role of divergent sensory drive in cichlid fish speciation
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Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex (RNA-Seq)
GEO Series GSE227382. Mus musculus. 96 samples. Type: Expression profiling by high throughput sequencing.
Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex (RNA-Seq II)
GEO Series GSE227342. Mus musculus. 79 samples. Type: Expression profiling by high throughput sequencing.
Sensory Stimulation During Simulated Driving
ClinicalTrials.gov study NCT03726840. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex (ChIP-Seq)
GEO Series GSE210656. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex
GEO Series GSE210658. Mus musculus. 183 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Sensory experience remodels genome architecture in neural circuit to drive motor learning
GEO Series GSE127995. Mus musculus. 154 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Sensory neurons drive pancreatic cancer progression through glutamatergic cancer-neuron pseudo-synapses
GEO Series GSE291933. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Glycogen Metabolism Drives Sensory Activation of Pomc Neurons
GEO Series GSE224274. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing; Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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