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47 results for “Auditory Cortex”
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
Data from: Stability of spontaneous, correlated activity in mouse auditory cortex
<p>Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system's topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.</p>
Intonational Speech Prosody Encoding in Human Auditory Cortex
<p>This dataset contains data and results associated with the manuscript, "Intonational Speech Prosody Encoding in Human Auditory Cortex", as well as code used to analyze the data and generate the figures of the manuscript. </p> <p>intonatang-2017.7.17.tar.gz contains the entire project, including neural data, stimulus sound files, analysis code, and documentation. The code and documentation can also be viewed on Github at https://github.com/ChangLabUcsf/intonatang.</p> <p>We additionally included each block of neural data and a zipped file containing the experimental, acoustic stimuli as separate files in this dataset. The data comprise three experiment types, "Speech", "Non-speech control", and "Non-speech missing f0 control". The neural data files are named with a subject identification number and a block number.</p> <p>Speech:</p> <ol> <li>EC113_B13</li> <li>EC113_B20</li> <li>EC113_B21</li> <li>EC118_B3</li> <li>EC118_B7</li> <li>EC118_B13</li> <li>EC122_B30</li> <li>EC122_B40</li> <li>EC122_B43</li> <li>EC122_B53</li> <li>EC123_B4</li> <li>EC123_B5</li> <li>EC123_B10</li> <li>EC125_B13</li> <li>EC125_B1044</li> <li>EC129_B10</li> <li>EC129_B16</li> <li>EC129_B37</li> <li>EC131_B47</li> <li>EC131_B48</li> <li>EC137_B7</li> <li>EC137_B10</li> <li>EC142_B36</li> <li>EC142_B37</li> <li>EC143_B9</li> <li>EC143_B11</li> <li>EC143_B13</li> </ol> <p>Non-speech control:</p> <ol> <li>EC122_B33</li> <li>EC122_B45</li> <li>EC123_B11</li> <li>EC123_B16</li> <li>EC125_B30</li> <li>EC129_B40</li> <li>EC129_B42</li> <li>EC131_B54</li> <li>EC131_B59</li> </ol> <p>Non-speech missing f0 control:</p> <ol> <li>EC137_B9</li> <li>EC137_B11</li> <li>EC142_B38</li> <li>EC142_B40</li> <li>EC143_B10</li> <li>EC143_B12</li> <li>EC143_B14</li> </ol> <p>These .mat files contain the following variables: </p> <ul> <li> badTimeSegments - (n_badTimeSegments x 2) <ul> <li>This variable contains manually marked time segments containing epileptiform, electrical, or movement artifacts. Each row indicates one bad time segment, with the start time and end time in seconds.</li> </ul> </li> <li>bcs - (n_bcs) <ul> <li>This array contains manually marked bad channels. These channels from the ECoG grid either had continuous epileptiform activity or signal indistinguishable from noise. The channels are indexed from 0.</li> </ul> </li> <li>ECXXX_BXX_hg_100Hz - (n_chans x n_timepoints) <ul> <li>This variable contains the mean high-gamma analytic amplitude signal for each channel, sampled at 100Hz. The mean is taken across 8 bands between 70-150Hz. The variable name contains the subject number, ECXXX, and block number BXX. </li> </ul> </li> <li>ECXXX_BXX_log_hg_100Hz - (n_chans x n_timepoints) <ul> <li>This variable contains the mean of the natural logarithm of the high-gamma analytic amplitude signal for each channel. The log is taken for each of the 8 bands between 70-150Hz and then averaged.</li> </ul> </li> <li>experiment <ul> <li>This variable holds the experiment type and is either "Speech", "Non-speech control", or "Non-speech missing f0 control".</li> </ul> </li> <li>sentence_numbers - (n_trials) <ul> <li>The integers in this array are the sentence number condition for each trial in this block. The sentence number conditions depend on the experiment type.</li> </ul> <ol> <li>For the "Speech" experiment, the four sentences indicated by 1, 2, 3, and 4 are "Humans value genuine behavior", "Movies demand minimal energy", "Lawyers give a relevant opinion", and "Reindeer are a visual animal".</li> <li>For the "Non-speech control" experiment, the sentence number conditions indicate which sentence from the main experiment the amplitude contour for the control stimuli came from. A sentence number of 5 means that the amplitude contour was flat. </li> <li>For the "Non-speech missing f0 control", the sentence number holds information about the composition of the stimulus (which harmonics were present), whether noise was added, and how much the pitch range was stretched. <ul> <li>0: 4h + 5h + 6h, no noise, stretch = 1</li> <li>1: f0 + 2h + 3h, no noise, stretch = 1</li> <li>2: 4h + 5h + 6h, noise, stretch = 1</li> <li>3: 4h + 5h + 6h, noise, stretch = 0.5</li> <li>4: 4h + 5h + 6h, noise, stretch = 2</li> </ul> </li> </ol> </li> <li>sentence_types - (n_trials) <ul> <li>The sentence type is the intonation contour condition. Across all experiment types, a sentence type of 1 is Neutral, 2 is Question, 3 is Emphasis 1, and 4 is Emphasis 3.</li> </ul> </li> <li>speakers - (n_trial) <ul> <li>The speaker conditions depend on the experiment. <ul> <li>The speaker condition for the "Speech" experiment is an integer between 1 and 3. 1 is the low-formant, low-pitch male speaker. 2 is the high-formant, high-pitch female speaker. 3 is the low-formant, high-pitch female speaker. The absolute pitch values of speakers 2 and 3 match, while the formant values of speaker 1 and 3 match.</li> <li>The speaker condition for both of the two non-speech experiments are either 1 or 2. 1 means low absolute pitch (male) and 2 means high absolute pitch (female).</li> </ul> </li> </ul> </li> <li>stims - (n_trials) <ul> <li>This array holds the stimulus name that was played for each trial. The names refer to the wav files in the tokens, tokens_nonspeech, and tokens_missing_f0 folders, for the "Speech", "Non-speech control", and "Non-speech missing f0 control" experiments, respectively.</li> </ul> </li> <li>times - (n_trials) <ul> <li>This array contains the onset times of each trial in seconds.</li> </ul> </li> </ul>
Data from: Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex
<p>This data set was analysed for the publication "Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex" by Lars Hausfeld, Lars Riecke and Elia Formisano (<a href="https://doi.org/10.1016/j.neuroimage.2018.02.065">10.1016/j.neuroimage.2018.02.065</a>). Anatomical and functional MRI was acquired at 7 Tesla and over the course of 3 sessions for each participant. Participants listened to natural auditory scenes consisting of three sound sources: voice, instrument and pure tone. To reveal effects of selective attention, participants were asked to listen to one of the three sources by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data for each participant are contained in the participant-specific folders S[participant id]_MRIdata.zip</p> <p>- Information and design, stimulation protocols and sounds are contained in DesignProtocolsSounds.zip</p> <p>- README files contain necessary information on the MRI acquisition, experimental design and the coding of conditions</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>In everyday life, we are often confronted with auditory scenes comprising multiple simultaneous sounds. When listening to two simultaneous talkers, the neural representation of the attended talker is selectively enhanced in auditory cortex. However, it remains unknown whether and how this selective attention mechanism operates on representations of different natural sound categories. In this high-field fMRI study we presented participants with simultaneous voices and musical instruments while manipulating their focus of attention. We found an attentional enhancement of neural sound representations in temporal cortex at locations that depended on the attended category (i.e., voices or instruments). In contrast, we found that in frontal cortex the site of enhancement was independent of the attended category and the same regions could flexibly represent any attended sound regardless of its category. These results are relevant to elucidate the interacting mechanisms of bottom-up and top-down processing during real-life audition.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p>
Multimodal mismatch responses in mouse auditory cortex
<div>All raw data and Matlab code necessary to produce the figures of https://elifesciences.org/reviewed-preprints/95398</div> <div> </div> <div> </div>
Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex
<p>Recent studies have shown that stimulus history can be decoded via the use of broadband sensory impulses to reactivate mnemonic representations. It has also been shown that predictive mechanisms in the auditory system demonstrate similar tonotopic organization of neural activity as that elicited by the perceived stimuli. However, it remains unclear if the mnemonic and predictive information can be decoded from cortical activity simultaneously and from overlapping neural populations. Here, we recorded neural activity using electrocorticography (ECoG) in the auditory cortex of anesthetized rats while exposed to repeated stimulus sequences, where events within the sequence were occasionally replaced with a broadband noise burst or omitted entirely. We show that both stimulus history and predicted stimuli can be decoded from neural responses to broadband impulse at overlapping latencies but linked to largely independent neural populations. We also demonstrate that predictive representations are learned over the course of stimulation at two distinct time scales, reflected in two dissociable time windows of neural activity. These results establish a valuable tool for investigating the neural mechanisms of passive sequence learning, memory encoding, and prediction mechanisms within a single paradigm, and provide novel evidence for learning predictive representations even under anaesthesia.</p>
Data from: Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex
<p>The brain predicts regularities in sensory inputs at multiple complexity levels, with neuronal mechanisms that remain elusive. Here, we monitored auditory cortex activity during the local-global paradigm, a protocol nesting different regularity levels in sound sequences. We observed that mice encode local predictions based on stimulus occurrence and stimulus transition probabilities, because auditory responses are boosted upon prediction violation. This boosting was due to both short-term adaptation and an adaptation-independent surprise mechanism resisting anesthesia. In parallel, and only in wakefulness, VIP interneurons responded to the omission of the locally expected sound repeat at sequence ending, thus providing a chunking signal potentially useful for establishing global sequence structure. When this global structure was violated, by either shortening the sequence or ending it with a locally expected but globally unexpected sound transition, activity slightly increased in VIP and PV neurons respectively. Hence, distinct cellular mechanisms predict different regularity levels in sound sequences.</p>
fUS imaging of ferret auditory cortex during passive listening of natural sounds
<p>Source data for paper: Distinct higher-order representations of natural sounds in human and ferret (BiorXiv, 2020), Landemard A, Bimbard C, Demené C, Shamma S, Norman-Haigneré S, Boubenec Y.</p> <p>This data repository contains several folders:<br>- <em>fUSData </em>contains raw data for all recording sessions. Information on data formatting can be found in README_fUSData text file.<br>- <em>Analysis</em> contains processed and denoised data. This data can be readily used to produce our figures using our publicly available scripts. This data can also be re-generated using data from <em>fUSData </em>folder using our denoising scripts. <br>- <em>AdditionalData </em>contains additional files necessary to run some of the analyses. </p> <p>Code implementing our denoising procedure and reproducing figures from the paper can be found on <a href="http://github.com/agneslandemard/naturalsounds_analysis">https://github.com/agneslandemard/naturalsounds_analysis </a></p> <p> </p>
Data from: Nonlinear decoding models enable music reconstruction from human auditory cortex activity
<p>This dataset is associated with the manuscript "Nonlinear decoding models enable music reconstruction from human auditory cortex activity", and provides all preprocessed files necessary to replicate the results.</p> <p>In this study, we recorded 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>
Sleep-like changes in neural processing emerge during sleep deprivation in early auditory cortex - Dataset
<p>Dataset summarizing for every State X Unit the auditory response features + the statistical models for all the different figures.</p> <p><br> Variables:</p> <p>corticosteroneRawData:the corticosterone measures in 12 animals across the five conditions described in the paper.</p> <p>statistics: statistical models for every figure</p> <p>isUnitValidForAnalysis: Logical vector with n units length describing which units were included in every analysis.</p> <p>resultsPerUnit: auditory response features per unit for all the different states/conditions described in the paper (Vigilant, Tired, NREM, REM, Q-Wakefulness during recovery sleep, movement-controlled Vigilant and Tired conditions).</p> <p>Each state is a struct with 496 entries (496 units). the different field in the struct are:</p> <p>- session: animal and session identifiers.</p> <p>- isContext: is Auditory Paradigm A (with 2,10,20,30,40 Hz Click Trains) or B (just 40 Hz click trains.</p> <p>- ch: recording channel/microwire # (1-16)</p> <p>- clus: cluster number in channel</p> <p>- clusType: is single-unit / multi-unit</p> <p>- spikeShape: the average spike waveform</p> <p>- baseFr: unit's baseline firing rate</p> <p>- baseFanoFactor: fano factor of each unit's baseline firing rate (not used in paper).</p> <p>- baseIsiCv: baseline ISI (inter-spike interval) CV (coefficient of variation), not used in paper</p> <p>- basePopCoupling: baseline population coupling values</p> <p>- clicks40Hz: 40Hz click responses</p> <p>- clicksAll: responses to all click trains (2,10,20,30,40 Hz)</p> <p>- offState: post-onset and off-state analysis.</p> <p>- clicksSim: simulated click response to different click rates based on the onset+post onset responses as in Supp. Fig. 6.</p> <p>- PostOnsetYoked: Surrogate post-onset response following spontaneous bursts as in Fig. 5A-C</p> <p>- drc: responses to Dynamic random chords used to get tuning.</p> <p> </p> <p> </p> <p> </p>
Data from: Stability of spontaneous, correlated activity in mouse auditory cortex
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Data from: Midbrain encodes sound detection behavior without auditory cortex
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Data for: Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex
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Data from: Task-specific invariant representation in auditory cortex
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Auditory cortex shapes sounds responses in the inferior colliculus
The extensive feedback from the auditory cortex (AC) to the inferior colliculus (IC) supports critical aspects of auditory behavior, but has not been extensively characterized. Previous studies demonstrated that activity in IC is altered by focal electrical stimulation and pharmacological inactivation of AC, but these methods lack the ability to selectively manipulate projection neurons. We measured the effects of selective optogenetic modulation of cortico-collicular feedback projections on IC sound responses in mice. Activation of feedback increased spontaneous activity and decreased stimulus selectivity in IC, whereas suppression had no effect. To further understand how microcircuits in AC may control collicular activity, we optogenetically modulated different cortical neuronal subtypes, specifically parvalbumin-positive (PV) and somatostatin-positive (SOM) inhibitory interneurons. We found that modulating either type of interneuron did not affect IC sound-evoked activity. Combined, our results identify that activation of excitatory projections, but not inhibition-driven increases in cortical activity, affects collicular sound responses.
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.
Human data - Functionally homologous representation of vocalizations in the auditory cortex ofhumans and macaques
<p>Human dataset used in the article Bodin et al., Functionally homologous representation of vocalizations in the auditory cortex of humans and macaques, Current Biology (2021), https://doi.org/10.1016/j.cub.2021.08.043</p>
Macaque data - Functionally homologous representation of vocalizations in the auditory cortex ofhumans and macaques
<p>Macaque dataset used in the article Bodin et al., Functionally homologous representation of vocalizations in the auditory cortex of humans and macaques, Current Biology (2021), https://doi.org/10.1016/j.cub.2021.08.043</p>
Auditory cortex single unit population activity during natural sound presentation -- dataset
<p><strong>Overview</strong></p> <p>High-density multi-channel neurophysiology data were collected from primary (A1) and secondary (PEG) fields of auditory cortex of passively listening ferrets during presentation of a large natural sound library. Single unit spikes were sorted using Kilosort. This dataset includes spike times for 849 A1 units and 398 PEG units. Stimulus waveforms were transformed to log-spaced spectrograms for analysis (18 channels, 10 ms time bins). Data set includes raw sound waveforms as well and high resolution (1000 samples/sec) single-trial spike data. The authors request that any publication using this data cite the following work: https://www.biorxiv.org/content/10.1101/2022.06.10.495698v2</p> <p>Version 1.1 is updated with more examples and documentation. It also includes a less-processed version of the spike data that permits reconstruction of the experimental sequence used at each recording site and single-trial responses to the repeated validation stimuli.</p> <p><strong>Data format/description</strong></p> <p>Preprocessed neural data are aggregated in two main files. All recordings were performed during presentation of the same natural sound library to passively listening animals. During each experiments, stimuli were presented in a random order, and repeated validation stimuli were interleaved throughout the experiment. In the main files, data have been aligned to the same order by stimulus and averaged across repeated presentations (for the validation stimuli, which were presented 20 times during each experiment). The averaged validation data make up the first 27 seconds of each recoding block.</p> <ul> <li><strong>A1_NAT4_ozgf.fs100.ch18.tgz</strong> - data from 849 A1 single units and log spectrogram of stimuli aligned with spike times. Data are aggregated across 64- or 128-channel recordings from 22 sites in 4 animals.</li> <li><strong>PEG_NAT4_ozgf.fs100.ch18.tgz</strong> - data from 398 PEG single units and log spectrogram of stimuli aligned with spike times. Data are aggregated across 64-channel recordings from 12 sites in 2 animals.</li> </ul> <p>Raw sound files (44100/s sampling, wav format) and spike times (1K/s sampling, in the original experimental order) are also provided in separate files. Summary data of model performance from the paper are also included.</p> <ul> <li><strong>wav.zip</strong> - raw wav files. As of version 2 of this repository, the wav files have been truncated to the 1-sec duration that was used in the experiments</li> <li><strong>A1_single_sites.zip</strong>, <strong>PEG_single_sites.zip</strong> - collections of files, one per recording site, with spike times stored in the actual order of data collection (including interleaved repeated validation stimuli). These spikes have been binned at 100 Hz and sorted to have matched order across all sites in the processed files (<strong>A1_NAT4_ozgf.fs100.ch18.tgz</strong>, <strong>PEG_NAT4_ozgf.fs100.ch18.tgz</strong>, respectively).</li> <li><strong>A1_pred_correlation.csv</strong>, <strong>PEG_pred_correation.csv</strong> - Comma-separated value file containing cross-validated prediction accuracy for each A1, PEG unit for each of the five exemplar models. The "sig_auditory" column is true for all units classified as having significant auditory responses, as classified in the publication.</li> </ul> <p><strong>Example scripts</strong></p> <p>Python scripts included with this dataset demonstrate how to load the neural data and perform a CNN model fit. Running the scripts requires the NEMS0 python library, which is available open source at <a href="http://github.com/lbhb/NEMS0">https://github.com/lbhb/NEMS0</a>.</p> <p><em>Quick install</em></p> <p>Create and activate a new conda environment:</p> <blockquote> <p>conda create -n NEMS0 python=3.7<br> conda activate NEMS0</p> </blockquote> <p>Download NEMS0:</p> <blockquote> <p>git clone https://github.com/lbhb/NEMS0</p> </blockquote> <p>Install NEMS0:</p> <blockquote> <p>pip install -e NEMS0</p> </blockquote> <p>Detailed instructions for installing NEMS0 are available in the Github repository (https://github.com/lbhb/NEMS0).</p> <p><em>Demo scripts</em></p> <p>Once NEMS0 is installed and the data are downloaded, move to the directory where the data and demo scripts are stored and run them in a NEMS0 environment.</p> <ul> <li><strong>pop_cnn_load.py </strong>- Load the A1 data and compare predictions for two neurons (Fig 3) by two population models (stage 1 fit complete). Illustrates how to load the data using Python.</li> <li><strong>pop_cnn_fit.py</strong> - Load a pre-fit A1 population model (stage 1) and complete stage 2 fit (refinement) for a single neuron. Illustrates use of NEMS0 for CNN model fitting.</li> <li><strong>single_trial_demo.py</strong> - Script demonstrating how to load the single trial data for a repeated validation stimulus from one A1 neuron. Also how to compute the average population PSTH for a single validation stimulus at 1000 sec-1 sampling. Unzip <strong>A1_single_sites.zip</strong> in the director containing this script first in order for it to run correctly.</li> </ul> <p><strong>Funding</strong></p> <p>Data collection, software development and processing were supported by funding from the NIH (R01DC014950, R01EB028155).</p>
Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement
<p>Source data used in the paper: "Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement" (BiorXiv, 2022), Chillale RK, Shamma S, Ostojic S, Boubenec Y. (doi: https://doi.org/10.1101/2022.12.19.521141)</p> <p>This data repository contains following folders: <br> - Pelardon (Ferret-P) and Timanoix(Ferret-T) as described in the figures of the paper<br> - Each folder contains sub-folders 1. Spike_soring and 2. TrialStructures and Channels_all.mat and SessionsInfo_AllSess.mat<br> - This data can used to generate figures in the paper using publicly available code (https://github.com/rupeshjnu/A1-Category) <br> - Spike_sorting folder contains recording sessions corresponding to SessionInfo_AllSess.mat file with the session number mentioned in the code<br> - Each Spike_sorting folder contains files<br> - TrialStructure folders corresponds to behavioral files corresponding to the recording sessions</p> <p><br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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
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.