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20 results for “Auditory Modeling”

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OpenNeuro52/100

Modeling an auditory stimulated brain under altered states of consciousness using the generalized ising model

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →
zenodo44/100

Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve

<p>Dataset&nbsp;containing the recorded and simulated data reported in the&nbsp;manuscript &quot;Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve&quot; published in the&nbsp;Journal of the Association for Research in Otolaryngology, JARO (<a href="https://doi.org/10.1007/s10162-019-00721-7">https://doi.org/10.1007/s10162-019-00721-7</a>):</p> <ol> <li>RECORDED Envelope Following Responses (EFR) in normal-hearing (NH) threshold and hearing-impaired (HI) human listeners using deeply (m = 85%) and shallowly (m = 25%) modulated sinusoidally amplitude modulated (SAM) tones.</li> <li>SIMULATED EFRs using the auditory nerve (AN) model by&nbsp;Zilany et al. (2009, 2014).</li> </ol> <p>Files content and structure:</p> <p><strong>Recorded EFRs</strong></p> <p><strong>Fig. 2:</strong></p> <ul> <li><em>fig2__recorded_efr.csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> EFR recordings as a function of stimulus level (EFR magnitude-level&nbsp;functions)&nbsp;for the NH and HI listeners using two modulation depths.</li> </ul> <p>The file&nbsp;containing the recorded EFR data have the following columns:</p> <ul> <li><em>lvl</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Stimulation level</li> <li><em>m85_ok: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>EFR magnitude (dB re to 1&nbsp;&micro;V) using m =&nbsp;85%. Significant responses (F-test = 1)</li> <li><em>m85_ko: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>EFR magnitude (dB re to 1&nbsp;&micro;V) using m =&nbsp;85%. Non-significant responses (F-test = 0)</li> <li><em>m85_bkg:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</em>Estimated background noise magnitude (dB re to 1&nbsp;&micro;V) for&nbsp;the recordings when m = 85%</li> <li><em>m25_ok: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>EFR magnitude (dB re to 1&nbsp;&micro;V) using m = 25%. Significant responses (F-test = 1)</li> <li><em>m25_ko: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>EFR magnitude (dB re to 1&nbsp;&micro;V) using m = 25%. Non-significant responses (F-test = 0)</li> <li><em>m25_bkg:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</em>Estimated background noise magnitude (dB re to 1&nbsp;&micro;V) for&nbsp;the recordings when m = 25%&nbsp;</li> <li><em>subj: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>Listener id</li> <li>hearing: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Hearing group (nh | hi) of the listener</li> </ul> <p><strong>Simulated EFRs:</strong></p> <p><strong><em>Files with the simulation results summing across frequency and SR fiber type</em></strong></p> <p><strong>Fig. 4:</strong></p> <ul> <li><em>fig4a__simul_efr__nh_23ohc_13ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level&nbsp;function for the NH threshold listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4a).</li> <li><em>fig4b__simul_efr__nh_slp_thres_all_ohc__no_cs.csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of OHC loss&nbsp;(Fig. 4b).</li> <li><em>fig4c__simul_efr__nh_slp_thres_all_ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of IHC loss&nbsp;(Fig. 4c).</li> <li><em>fig4d__simul_efr__hi_23ohc_13ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level&nbsp;function for the HI listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4e__simul_efr__hi_all_ohc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming&nbsp;all of OHC loss&nbsp;(Fig. 4e).</li> <li><em>fig4f__simul_efr__hi_all_ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of IHC loss&nbsp;(Fig. 4f).</li> <li><em>fig4g__simul_efr__hi_slp_thres_23ohc_13ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level&nbsp;function for the HI listeners (average) assuming&nbsp;sloping threshold at EHF&nbsp;and 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4h__simul_efr__hi_slp_thres_all_ohc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF&nbsp;and all of OHC loss&nbsp;(Fig. 4e).</li> <li><em>fig4i__simul_efr__hi_slp_thres_all_ihc__no_cs.csv</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF&nbsp;and and all of IHC loss&nbsp;(Fig. 4f).</li> </ul> <p>&nbsp;</p> <p><strong>Fig. 5:</strong></p> <ul> <li><em>fig5a__simul_efr__nh__cs_ms_ls_100p.csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming&nbsp;cochlear synaptopathy (CS) of a 100% of loss of only medium- and low-spontaneous rate (SR)&nbsp;AN fibers&nbsp;(Fig. 5a).</li> <li><em>fig5b__simul_efr__nh09_cs__approx</em>.<em>csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function to approximate the data for the NH threshold listener NH09 including CS (Fig. 5b).</li> <li><em>fig5c__simul_efr__hi04_cs__approx</em>.<em>csv</em>: &nbsp;<br> Simulated EFR magnitude-level function to approximate the data for the HI listener HI04&nbsp;including CS (Fig. 5c).</li> </ul> <p>&nbsp;</p> <p><strong>Fig. 6:</strong></p> <p>Files with the simulation results for the NH threshold listener&nbsp;in different characteristic frequency (CF) bands and SR fiber types</p> <ul> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m12.mat</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 12%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m25.mat</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 25%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m50.mat</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 50%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m85.mat</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 85%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m100.mat</em>: &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 100%.</li> </ul> <p>&nbsp;</p> <p><strong>Fig. 7:</strong></p> <ul> <li><em>fig7a__simul_efr__bw_analys__32oct_[20, 40, 60, 80, 100]p.csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming&nbsp;CS of a bandwidth (BW) of 3/2-octave for a loss of AN fibers ranging from 20% to&nbsp;100% (Fig. 7a).</li> <li><em>fig7b__simul_efr__bw_analys__1oct_[20, 40, 60, 80, 100]p.csv</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming&nbsp;CS of a bandwidth (BW) of 1-octave for a loss of AN fibers ranging from 20% to&nbsp;100% (Fig. 7b).</li> <li><em>fig7c__simul_efr__bw_analys__13oct_[20, 40, 60, 80, 100]p.csv</em>:<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming&nbsp;CS of a bandwidth (BW) of 1/3-octave for a loss of AN fibers ranging from 20% to&nbsp;100% (Fig. 7c).</li> </ul> <p>&nbsp;</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>&nbsp;</p> <p>The structure of the .csv files that contain the EFR simulations is:</p> <ul> <li><em>lvl</em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Stimulation level</li> <li><em>mgn_mxxx: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>Simulated EFR magnitude (a.u. in dB) for each modulation depth (100%, 85%, 50%, 25% and 12%)</li> <li><em>bkg_mxxx: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</em>Estimate of the background noise floor (a.u. in dB)&nbsp;for each modulation depth.&nbsp;</li> <li><em>ftest_mxxx: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>Result of the F-test statistical test&nbsp;(0 or 1) for each modulation depth.</li> </ul> <p>&nbsp;</p> <p>The structure of the .mat&nbsp;files that contain the EFR simulations is:</p> <ul> <li><em>exper_type</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Name of the simulated experiment</li> <li><em>species</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Species used in the AN model (in this study is always 2: human)</li> <li><em>species_age</em>:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(Not used in this work). Age of the animal when species is 4: mouse</li> <li>modulation<em>: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>Modulation depth of the SAM tone used in the simulation <em>(100%, 85%, 50%, 25% or 12%)</em></li> <li><em>f_on: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em>Center frequency of the on-frequency band (<em>2000 Hz</em>)</li> <li><em>f_off_hf: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</em>Center frequency of the first off-frequency band (<em>3000 Hz</em>)</li> <li><em>f_off_vhf: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; </em>Center frequency of the second off-frequency band (<em>7000 Hz</em>)&nbsp;</li> <li><em>f_off_uvhf: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; </em>Center frequency of the third&nbsp;off-frequency band (<em>12000 Hz</em>)</li> <li><em>lvl_vect: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; </em>Stimulus level vector&nbsp;<em>(from 5 to 100 dB SPL, in steps of 5 dB)</em></li> <li><em>simul_efr &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</em>&nbsp;&nbsp;Structure with the simulated data <ul> <li>The data structure contains many fields which are&nbsp;matricies of size 3x20. The columns are the 20 stimulus levels defined in <em>lvl_vect</em>, and the first row is the simulated EFR, the second row is the estimates background noise floor in the simulation, and the third row is the output of the F-test statistics.</li> <li>The structure fields can be divided in&nbsp;4 groups: <ul> <li><em>ihc_</em> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Responses from the IHC (not shown in the paper)</li> <li><em>an_hs_</em> &nbsp; &nbsp; &nbsp;Responses from the High-SR fibers in the AN</li> <li><em>an_ms_</em> &nbsp; &nbsp; Responses from the Medium-SR fibers in the AN</li> <li><em>an_ls_</em> &nbsp; &nbsp; &nbsp; Responses from the Low-SR fibers in the AN</li> </ul> </li> <li>Each group has 5 responses corresponding to the on-frequency band (<em>_on</em>) and the three off-frequency bands (<em>_off_hf</em>, <em>_off_vhf</em>, <em>_off_uvhf</em>); and the sum across frequencies (<em>_across_f</em>)</li> </ul> </li> </ul>

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

Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox

<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled &quot;Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox&quot; by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on &#39;king2019&#39; (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type &#39;startup_fastACI;&#39;, without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox.&nbsp;&nbsp; &nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2023View details →
zenodo40/100

Speech and noise mixtures used in Modelling Auditory Processing and Organisation

<p>Speech and noise signals used in Cooke, M (1991)&nbsp;Modelling Auditory Processing and Organisation, Ph. D. Thesis, Department of Computer Science,&nbsp;University of Sheffield</p>

opencc-by-4.0Nov 2019View details →
zenodo36/100

Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices

<p>The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled &quot;Consonant-in-noise discrimination using an auditory model with different speech-based decision devices&quot; by the same authors, presented in the DAGA conference in Vienna, Austria,&nbsp;on 17/08/2021.&nbsp;</p> <p>The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure:</p> <ul> <li><strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises</li> <li><strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI).</li> </ul> <p>To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect&nbsp; new data for participant &#39;S03&#39;, you have to input the following command in MATLAB:</p> <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>

opencc-by-4.0Aug 2021View details →
zenodo36/100

Data from: Nonlinear decoding models enable music reconstruction from human auditory cortex activity

<p>This dataset is associated with the manuscript &quot;Nonlinear decoding models enable music reconstruction from human auditory cortex activity&quot;, and provides all preprocessed files necessary to replicate&nbsp;the results.</p> <p>In this study, we recorded&nbsp;intracranial EEG data (specifically, ECoG) in 29 patients with pharmacoresistant epilepsy while they were passively listening to a Pink Floyd song.</p> <p>The present dataset consists of preprocessed neural activity (High-Frequency Activity, 70-150 Hz), electrode coordinates (in MNI template space) and auditory stimulus (raw wave file, and 32- and 128-frequency-bin auditory spectrogram). HFA and both auditory spectrograms have a sampling rate of 100 Hz, and are temporally aligned (duration of 190.72 s).</p> <p>The code we used to preprocess and analyze the data is hosted on GitHub, <a href="https://github.com/ludovicbellier/PF_HFAdecoding">here</a> for the manuscript and <a href="https://github.com/ludovicbellier/PF_HFAdecoding">there</a> for the predictive modeling functions.</p>

opencc-by-4.0Apr 2023View details →
ClinicalTrials.gov36/100

Models of Auditory Hallucination

ClinicalTrials.gov study NCT04210557. IPD Sharing: NO. Countries: 1. Publications: 54.

closedIPD-NOFeb 2026View details →
dryad32/100

Data from: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex

Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.

opencc-zeroDec 2018View details →
zenodo32/100

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (2/4)

<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here:&nbsp;<a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>2 out of 4</strong>. The other 3 uploads can be found at: &nbsp;</p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu&nbsp;&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo32/100

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (3/4)

<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here:&nbsp;<a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>3 out of 4</strong>. The other 3 uploads can be found at: &nbsp;</p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu&nbsp;&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo32/100

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (1/4)

<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here:&nbsp;<a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>1 out of 4</strong>. The other 3 uploads can be found at: &nbsp;</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu&nbsp;&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo32/100

Matlab code and datasets for: Auditory model-based parameter estimation and selection of the most informative experimental conditions

<p>The dataset and the matlab code used for the publication:&nbsp;Auditory model-based parameter estimation and selection of the most informative experimental conditions</p>

opencc-by-4.0Apr 2023View details →
ClinicalTrials.gov32/100

Dexmedetomidine PKPD Modeling and the Influence of Auditory Stimulation on Dexmedetomidine Effect

ClinicalTrials.gov study NCT01879865. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Data from: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex

Open the record for dataset details and reuse information.

publicJan 2019View details →
geo24/100

miR-338-3p controls the late onset of auditory thalamocortical disruption in schizophrenia models

GEO Series GSE73981. Mus musculus. 56 samples. Type: Non-coding RNA profiling by array.

openGEO-OpenOct 2016View details →
geo24/100

Ignition of supporting cell activation for hair cell regeneration in the avian auditory epithelium: an explant culture model

GEO Series GSE154375. Gallus gallus. 4 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenOct 2020View details →
zenodo24/100

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (4/4)

<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here:&nbsp;<a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>4 out of 4</strong>. The other 3 uploads can be found at: &nbsp;</p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu&nbsp;&nbsp;</p>

opencc-by-4.0Nov 2023View details →
geo20/100

Cell-specific delivery of GJB2 restores auditory function in mouse models of DFNB1 deafness and mediates appropriate expression in NHP cochlea

GEO Series GSE308613. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenOct 2025View details →
zenodo16/100

Auditory stream segregation can be modeled by neural competition in cochlear implant listeners

<p>This repository contains the simulations presented in the article &quot;Auditory stream segregation can be modeled by neural competition in cochlear implant listeners&quot; with DOI <strong>(add DOI here)</strong></p> <p>The repository contains 2 mat files and the matlab model object to handle them:</p> <ul> <li>pSeg__sigma_40__L_06.mat: This file contains the simulations from the neuromechanistic model with sigma = 40 and L = 0.6</li> <li>pSeg__sigma_30__L_035.mat: This file contains the simulations from the neuromechanistic model with sigma = 30 and L = 0.35</li> <li>PSegregated.m: This file defines the matlab model object.<br> &nbsp;</li> </ul>

restrictedFeb 2019View details →
geo12/100

microRNA expression data of ascencding central auditory pathway from noise-induced hearing loss rat model

GEO Series GSE271449. synthetic construct; Rattus norvegicus. 64 samples. Type: Non-coding RNA profiling by array.

openGEO-OpenJul 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

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Annotated Behaviour and Observability Dataset (ABODe)

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behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
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DANDI Archive for NWB datasets

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record