Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,960
datasets available to search
ShareScore release 0.9.0
Dataset results
1,960 results for “Cortex”
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
Open the record for dataset details and reuse information.
The Interplay between Hebbian and homeostatic plasticity in the Adult Visual cortex
<p>Data linked to the article "The interplay between Hebbian and homeostatic plasticity in the adult visual cortex", Journal of Physiology, DOI: <a href="https://doi.org/10.1113/JP287665">https://doi.org/10.1113/JP287665</a></p> <p>Data from binocular rivalry measurements and processed data from EEG Visual Evoked Potentials (VEP) are separated in different files.</p> <p>The ocular dominance index (ODI) files are split in two: the "ODI_values" file contains the raw measurements from participants, and the "change_from_baseline file" contains the same data normalized to baseline for each measurement.</p> <p>In both files, each column refers to a different measurement and condition:</p> <p>noHFS: data measured with the 17Hz HFS block before monocular deprivation<br>HFS: data measured with the 8.6Hz HFS block before monocular deprivation</p> <p>Baseline: Ocular dominance index measured at the start of the session, before any manipulation<br>Post_MD_1: first measurement after 60 minutes monocular deprivation (starting immediately after the end of deprivation)<br>Post_MD_2: second measurement after 60 minutes monocular deprivation (starting 11 minutes after the end of deprivation)<br>Post_MD_3: third measurement after 60 minutes monocular deprivation (starting 22 minutes after the end of deprivation)</p> <p>In VEP files, each column refers to a different condition:</p> <p>HFS: VEP recorded in the high-frequency stimulation condition, no monocular deprivation<br>HFS_MD: VEP recorded in the high-frequency stimulation condition with monocular deprivation<br>noHFS: VEP recorded in the condition where the HFS block was withheld, as a control for its role in our effect</p> <p>pre: first 500 measurements, before the High-Frequency Stimulation (HFS) block<br>post: last 500 measurements, after the HFS block (or after the break in the noHFS condition).</p>
Decoding of multisensory semantics and memories in low-level visual cortex
Open the record for dataset details and reuse information.
Cognitive control of sensory pain encoding in the pregenual anterior cingulate cortex. d1 - decoder construction in day 1, d2 - adaptive control in day 2.
Open the record for dataset details and reuse information.
Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations
Open the record for dataset details and reuse information.
Effects of Phase Regression on High-Resolution Functional MRI of the Primary Visual Cortex
Open the record for dataset details and reuse information.
Developmental change in prefrontal cortex recruitment supports the emergence of value-guided memory
Open the record for dataset details and reuse information.
Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
A whole-cortex probabilistic diffusion tractography connectome
<p>This is a collection of the results data for the eNeuro article of the same name, <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. Please cite this publication when using these data. Files with the .mat extension are matlab v7.3 files. The raw data from which these data were derived are available from <a href="https://db.humanconnectome.org">https://db.humanconnectome.org</a> and <a href="https://f-tract.eu">https://f-tract.eu</a>.</p>
Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus
<p>This is supplementary data and source data for the manuscript, <em>"Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus"</em>.</p> <p><strong>Abstract</strong>: Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n=399, dorsolateral prefrontal cortex: n=377, and hippocampus: n=394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.</p>
Phase coding of spatial representations in the human entorhinal cortex
<p>Supporting Preprocessed Electrophysiology Data for the article titled "Phase coding of spatial representations in the human entorhinal cortex".</p> <p>Data were preprocessed and analyzed using Matlab.</p> <p>Data, after decompression, are organized hierarchically in folders and subfolders (two levels).</p> <p>Main folder names are composed as subjectID_date_EC_DATA_taskNo</p> <p>Sub-folders named as: CHn_single-unit ID </p> <p>[subjectID, date, task num] + [Electrode channel, and single-unit ID] </p> <p>subjectID: (subject1, subject2)</p> <p>date: mmm-dd</p> <p>task: (1,2,3,4) Virtual environments (1: backyard, 2: Louvre, 3: Luxor, 4: desert)</p> <p>electrode Channel: CH1,CH2, CH3, CH4, CH5</p> <p>single-unit ID: 0, 1, 2, 3, 4, 5, 6</p> <p>For each channel and single-unit, the following 9 datasets were computed and saved. For instance, for the first electrode and first single-unit class (CH=1, cell ID= 0):</p> <p>CH1_Clu0.mat -- Summary of firing features of this single-unit including firing rate, and grid score of the cell. (In Matlab mat format)<br> CH1_Clu0_MeanPhaseMap.csv -- Mean spike phase map relative to gamma-band LFP<br> CH1_Clu0_Phase.csv -- Spike phase CH1_Clu0_spikeData.csv<br> CH1_Clu0_spkT.csv -- Spike times in increental order<br> CH1_Clu0_VarPhaseMap.csv -- Map of the variance of spike times in increental order<br> CH1_Clu0_xval.mat -- Summary of firing features of 50% of single-unit spikes (every other spike) for cross-validation purposes. (In Matlab mat format)<br> CH1_Clu0_xyPos.csv -- X,Y coordinates of the avatar's position in 1 ms resolution. In other words, the path taken by the avatar sampled at 1 kHz.<br> CH1_Clu0_XYspkT.csv -- X,Y coordinates of the avatar at moments of spikes. In other words, the location in space where a spike was fired by the putative neuron.</p> <p> </p>
PsPM-FSS7B: Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex
<p>This dataset includes skin conductance response (SCR), electromygoram (EMG), and pupil size response (PSR) measurements. Also included are CS and US information, keypress responses, keypress response times and key correctness. The dataset contains data from 68 healthy unmedicated participants (34 females) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Simple and complex CS are deliverd to the intermediate phalanges of the index and middle fingers of the nondominant hand. Simple stimulis are stimulations to either index or middle finger, complex stimulis are stimulations of different temporal structure to both index and middle fingers. CS intensity is set to a perceivable but not unpleasant level. US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s. The study included fear acquisition (day 1), recall and retest sessions (day 2). Participants are divided into experimental and control groups, where experimental group received continuous theta-burst stimulation on primary somatosensory cortex contralateral to the CS hand immediately prior to fear acquisition, and control group received the same stimulation to the primary somatosensory cortex ipsilateral to the CS hand. For SCR data from the acquisition session, there are 62 datasets (28 experimental, 34 control), and for PSR, 37 datasets (20 experimental, 17 control). For EMG data from the recall session, there are 52 datasets (25 experimental, 27 control). For SCR data from the retest session, there are 56 datasets (27 experimental, 39 control), and for PSR, 42 datasets (22 experimental, 20 control). </p>
Praxis and language brain hemispheric activity data from Kroliczak, Buchwald, et al., 2021 - Cortex - publication
<p>Hemispheric activity fMRI data for praxis and language dataset from the Kroliczak, Buchwald, et al., article published in 2021 in the Cortex publication.</p> <p><strong>Cite as:</strong></p> <p>Kroliczak, G., Buchwald, M., Kleka, P., Klichowski, M., Potok, W., Nowik, A. M., ... & Piper, B. J. (2021). Manual praxis and language-production networks, and their links to handedness. <em>Cortex</em>, <em>140</em>, 110-127. <a href="https://doi.org/10.1016/j.cortex.2021.03.022">https://doi.org/10.1016/j.cortex.2021.03.022</a></p> <p>Link to the publication: <a href="https://www.sciencedirect.com/science/article/pii/S0010945221001337">https://www.sciencedirect.com/science/article/pii/S0010945221001337</a></p> <p>The complete dataset for this publication was published at OSF.io: <a href="https://osf.io/63hjt/">https://osf.io/63hjt/</a></p>
A mesial-to-lateral dissociation for orthographic processing in the visual cortex
Open the record for dataset details and reuse information.
Adaptive memory distortions are predicted by feature representations in parietal cortex
Open the record for dataset details and reuse information.
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology
<p><strong>General Description.</strong> This dataset consists of:</p> <ol> <li>The threshold crossing times of extracellularly and simultaneously recorded spikes, sorted into units (up to five, including a "hash" unit), along with sorted waveform snippets, and,</li> <li>The x,y position of the fingertip of the reaching hand and the x,y position of reaching targets (both sampled at 250 Hz).</li> </ol> <p>The behavioral task was to make self-paced reaches to targets arranged in a grid (e.g. 8x8) without gaps or pre-movement delay intervals. One monkey reached with the right arm (recordings made in the left hemisphere); The other reached with the left arm (right hemisphere). In some sessions recordings were made from both M1 and S1 arrays (192 channels); in most sessions M1 recordings were made alone (96 channels).</p> <p>Data from two primate subjects are included: 37 sessions from monkey 1 ("Indy", spanning about 10 months) and 10 sessions from monkey 2 ("Loco", spanning about 1 month), for a total of ~ 20,000 reaches and 6,500 reaches from monkeys 1 and 2, respectively.</p> <p><strong>Possible uses. </strong>These data are ideal for training BCI decoders, in particular because they are not segmented into trials. We expect that the dataset will be valuable for researchers who wish to design improved models of sensorimotor cortical spiking or provide an equal footing for comparing different BCI decoders. Other uses could include analyses of the statistics of arm kinematics, spike noise-correlations or signal-correlations, or for exploring the stability or variability of extracellular recording over sessions.</p> <p><strong>Variable names. </strong>Each file contains data in the following format. In the below, <em>n</em> refers to the number of recording channels, <em>u</em> refers to the number of sorted units, and <em>k</em> refers to the number of samples.</p> <ul> <li>chan_names - n x 1 <ul> <li>A cell array of channel identifier strings, e.g. "<em>M1 001</em>".</li> </ul> </li> <li>cursor_pos - k x 2 <ul> <li>The position of the cursor in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>finger_pos - k x 3 <em>or </em>k x 6 <ul> <li>The position of the working fingertip in Cartesian coordinates (z, -x, -y), as reported by the hand tracker in cm. Thus the cursor position is an affine transformation of fingertip position using the following matrix:<br>\(\begin{pmatrix} 0 & 0 \\ -10 & 0 \\ 0 & -10 \end{pmatrix}\)<br>Note that for some sessions finger_pos includes the orientation of the sensor as well; the full state is thus: (z, -x, -y, azimuth, elevation, roll).</li> </ul> </li> <li>target_pos - k x 2 <ul> <li>The position of the target in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>t - k x 1 <ul> <li>The timestamp corresponding to each sample of the cursor_pos, finger_pos, and target_pos, seconds.</li> </ul> </li> <li>spikes - n x u <ul> <li>A cell array of spike event vectors. Each element in the cell array is a vector of spike event timestamps, in seconds. The first unit (<em>u</em>1) is the "unsorted" unit, meaning it contains the threshold crossings which remained after the spikes on that channel were sorted into other units (<em>u</em>2, <em>u</em>3, etc.) For some sessions spikes were sorted into up to 2 units (i.e. <em>u</em>=3); for others, 4 units (<em>u</em>=5).</li> </ul> </li> <li>wf - n x u <ul> <li>A cell array of spike event waveform "snippets". Each element in the cell array is a matrix of spike event waveforms. Each waveform corresponds to a timestamp in "spikes". Waveform samples are in microvolts.</li> </ul> </li> </ul> <p><strong>Decoder Results.</strong> These data were used to fit decoder models, as reported in Makin, et al [1]. To aid comparisons to other decoders, we include performance summaries (for each session, decoder, bin-width, etc.) in the file <em>refh_results.csv</em>, containing the following columns:</p> <ul> <li>session - a session identifier, e.g. "indy_20160407_02"</li> <li>monkey - one of, "indy" or "loco"</li> <li>num_neurons - total number of features used in the decoder</li> <li>num_training_samples - number of samples (at the specified bin-width) used to train the decoder (sequential, from file start)</li> <li>num_testing_samples - number of samples used to evaluate the decoder (sequential, until file end)</li> <li>kinematic_axis - one of, "posx", "posy", "velx", "vely", "accx" or "accy"</li> <li>bin_width - one of, "16", "32", "64" or "128"</li> <li>decoder - one of, "regression", "KF_observed", "KF_static", "KF_dynamic", "UKF", "rEFH_static" or "rEFH_dynamic"</li> <li>rsq - coefficient of determination, R2</li> <li>snr - Signal to noise ratio, SNR := -10 log10(1 - R2)</li> </ul> <p><strong>Videos. </strong>For some sessions, we recorded screencasts of the stimulus presentation display using a dedicated hardware video grabber. These screencasts are thus a faithful representation of the stimuli and feedback presented to the monkey and are available for the following sessions:</p> <ul> <li><a href="https://youtu.be/bPkpdpm03z8">indy_20160921_01</a></li> <li><a href="https://youtu.be/B02z6w4c3yk">indy_20160930_02</a></li> <li><a href="https://youtu.be/S640zzIKJs8">indy_20160930_05</a></li> <li><a href="https://youtu.be/tRoe84E0AzA">indy_20161005_06</a></li> <li><a href="https://youtu.be/hNZlBa516jM">indy_20161006_02</a></li> <li><a href="https://youtu.be/L6GKwI2u1Es">indy_20161007_02</a></li> <li><a href="https://youtu.be/eV1joYU5vt0">indy_20161011_03</a></li> <li><a href="https://youtu.be/4LM_gKt2cYg">indy_20161013_03</a></li> <li><a href="https://youtu.be/GLGrKHgf-zw">indy_20161014_04</a></li> <li><a href="https://youtu.be/6aPrv8HEPGQ">indy_20161017_02</a></li> </ul> <p><strong>Supplements. </strong>The raw broadband neural recordings that the spike trains in this dataset were extracted from are available for the following sessions:</p> <ul> <li>indy_20160622_01: <a href="https://doi.org/10.5281/zenodo.1488440">doi:10.5281/zenodo.1488440</a></li> <li>indy_20160624_03: <a href="https://doi.org/10.5281/zenodo.1486147">doi:10.5281/zenodo.1486147</a></li> <li>indy_20160627_01: <a href="https://doi.org/10.5281/zenodo.1484824">doi:10.5281/zenodo.1484824</a></li> <li>indy_20160630_01: <a href="https://doi.org/10.5281/zenodo.1473703">doi:10.5281/zenodo.1473703</a></li> <li>indy_20160915_01: <a href="https://doi.org/10.5281/zenodo.1467953">doi:10.5281/zenodo.1467953</a></li> <li>indy_20160916_01: <a href="https://doi.org/10.5281/zenodo.1467050">doi:10.5281/zenodo.1467050</a></li> <li>indy_20160921_01: <a href="https://doi.org/10.5281/zenodo.1451793">doi:10.5281/zenodo.1451793</a></li> <li>indy_20160927_04: <a href="https://doi.org/10.5281/zenodo.1433942">doi:10.5281/zenodo.1433942</a></li> <li>indy_20160927_06: <a href="https://doi.org/10.5281/zenodo.1432818">doi:10.5281/zenodo.1432818</a></li> <li>indy_20160930_02: <a href="https://doi.org/10.5281/zenodo.1421880">doi:10.5281/zenodo.1421880</a></li> <li>indy_20160930_05: <a href="https://doi.org/10.5281/zenodo.1421310">doi:10.5281/zenodo.1421310</a></li> <li>indy_20161005_06: <a href="https://doi.org/10.5281/zenodo.1419774">doi:10.5281/zenodo.1419774</a></li> <li>indy_20161006_02: <a href="https://doi.org/10.5281/zenodo.1419172">doi:10.5281/zenodo.1419172</a></li> <li>indy_20161007_02: <a href="https://doi.org/10.5281/zenodo.1413592">doi:10.5281/zenodo.1413592</a></li> <li>indy_20161011_03: <a href="https://doi.org/10.5281/zenodo.1412635">doi:10.5281/zenodo.1412635</a></li> <li>indy_20161013_03: <a href="https://doi.org/10.5281/zenodo.1412094">doi:10.5281/zenodo.1412094</a></li> <li>indy_20161014_04: <a href="https://doi.org/10.5281/zenodo.1411978">doi:10.5281/zenodo.1411978</a></li> <li>indy_20161017_02: <a href="https://doi.org/10.5281/zenodo.1411882">doi:10.5281/zenodo.1411882</a></li> <li>indy_20161024_03: <a href="https://doi.org/10.5281/zenodo.1411474">doi:10.5281/zenodo.1411474</a></li> <li>indy_20161025_04: <a href="https://doi.org/10.5281/zenodo.1410423">doi:10.5281/zenodo.1410423</a></li> <li>indy_20161026_03: <a href="https://doi.org/10.5281/zenodo.1321264">doi:10.5281/zenodo.1321264</a></li> <li>indy_20161027_03: <a href="https://doi.org/10.5281/zenodo.1321256">doi:10.5281/zenodo.1321256</a></li> <li>indy_20161206_02: <a href="https://doi.org/10.5281/zenodo.1303720">doi:10.5281/zenodo.1303720</a></li> <li>indy_20161207_02: <a href="https://doi.org/10.5281/zenodo.1302866">doi:10.5281/zenodo.1302866</a></li> <li>indy_20161212_02: <a href="https://doi.org/10.5281/zenodo.1302832">doi:10.5281/zenodo.1302832</a></li> <li>indy_20161220_02: <a href="https://doi.org/10.5281/zenodo.1301045">doi:10.5281/zenodo.1301045</a></li> <li>indy_20170123_02: <a href="https://doi.org/10.5281/zenodo.1167965">doi:10.5281/zenodo.1167965</a></li> <li>indy_20170124_01: <a href="https://doi.org/10.5281/zenodo.1163026">doi:10.5281/zenodo.1163026</a></li> <li>indy_20170127_03: <a href="https://doi.org/10.5281/zenodo.1161225">doi:10.5281/zenodo.1161225</a></li> <li>indy_20170131_02: <a href="https://doi.org/10.5281/zenodo.854733">doi:10.5281/zenodo.854733</a></li> </ul> <p><strong>Contact Information.</strong> We would be delighted to hear from you if you find this dataset valuable, especially if it leads to publication. Corresponding author: J. E. O'Doherty <joeyo@neuroengineer.com>.</p> <p><strong>Citation.</strong></p> <p>@misc{ODoherty:2017, author = {O'{D}oherty, Joseph E. and Cardoso, Mariana M. B. and Makin, Joseph G. and Sabes, Philip N.}, title = {Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex electrophysiology}, doi = {10.5281/zenodo.788569}, url = {https://doi.org/10.5281/zenodo.788569}, month = may, year = {2017} }</p> <p><strong>Publications making use of this dataset.</strong></p> <ol> <li>Makin, J. G., O'Doherty, J. E., Cardoso, M. M. B. & Sabes, P. N. (2018). Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm. <em>J Neural Eng.</em> 15(2): 026010. <a href="https://doi.org/10.1088/1741-2552/aa9e95">doi:10.1088/1741-2552/aa9e95</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2018). Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces. <em>2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Honolulu, HI, USA, 2018, pp. 2547-2550. <a href="https://doi.org/10.1109/EMBC.2018.8512830">doi:10.1109/EMBC.2018.8512830</a></li> <li>Balasubramanian, M., Ruiz, T., Cook, B., Bhattacharyya, S., Prabhat, Shrivastava, A. & Bouchard K. (2018). Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1808.06992">arXiv:1808.06992</a></li> <li>Sachdeva, P. S., Bhattacharyya, S., & Bouchard, K. E. (2019). Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso, <em>41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Berlin, Germany, pp. 1965-1968. <a href="https://doi.org/10.1109/EMBC.2019.8856316">doi:10.1109/EMBC.2019.8856316</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2019). End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network. <em>2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), </em>Nara, Japan, pp. 1-4. <a href="https://doi.org/10.1109/biocas.2019.8919131">doi:10.1109/biocas.2019.8919131</a></li> <li>Bose, S. K., Acharya, J., & Basu, A. (2019). Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering. <em>2019 53rd Asilomar Conference on Signals, Systems, and Computers</em>, Pacific Grove, CA, USA, pp. 1522-1527. <a href="https://doi.org/10.1109/IEEECONF44664.2019.9048891">doi:10.1109/IEEECONF44664.2019.9048891</a></li> <li>Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. (2019). Towards Intelligent Intra-cortical BMI (i2BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters. <em>bioRxiv Preprint.</em> 772988. <a href="https://doi.org/10.1101/772988">doi:10.1101/772988</a></li> <li>Keshtkaran, M. R., & Pandarinath, C. (2019). <a href="https://papers.nips.cc/paper/9722-enabling-hyperparameter-optimization-in-sequential-autoencoders-for-spiking-neural-data">Enabling hyperparameter optimization in sequential autoencoders for spiking neural data.</a> <em>Advances in Neural Information Processing Systems (NeurIPS) 32.</em></li> <li>Clark, D. G., Livezey, J. A., & Bouchard, K. E. (2019). Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1905.09944">arXiv:1905.09944</a></li> <li>Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. (2019). Towards Intelligent Intracortical BMI (i2BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters. <em>IEEE Transactions on Biomedical Circuits and Systems</em>. 13(6): 1615-1624. <a href="https://doi.org/10.1109/TBCAS.2019.2944486">doi:10.1109/TBCAS.2019.2944486</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2019). Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1901.00708">arXiv:1901.00708</a></li> <li>Balasubramanian, M., Ruiz, T., Cook, B., Prabhat, Bhattacharyya, S., Shrivastava, A. & Bouchard K. (2020). Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. <em>Proceeding of the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS). </em>New Orleans, LA, USA, pp. 264-273. <a href="https://doi.org/10.1109/IPDPS47924.2020.00036">doi: 10.1109/IPDPS47924.2020.00036</a></li> <li>Bose, S. K., Acharya, J. & Basu, A. (2020). Is my neural network neuromorphic? Taxonomy, recent trends and future directions in neuromorphic engineering. <em>arXiv Preprint</em>. <a href="https://arxiv.org/abs/2002.11945">arXiv:2002.11945</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Inferring entire spiking activity from local field potentials with deep learning. <em>bioRxiv Preprint.</em> 2020.05.02.074104. <a href="https://doi.org/10.1101/2020.05.02.074104">doi:10.1101/2020.05.02.074104</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning. <em>TechRxiv Preprint.</em> <a href="https://doi.org/10.36227/techrxiv.12383600.v1">doi:10.36227/techrxiv.12383600.v1</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning. <em>bioRxiv Preprint.</em> 2020.05.07.083063 <a href="https://doi.org/10.1101/2020.05.07.083063">doi:10.1101/2020.05.07.083063</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis. C.-S. (2020). Impact of referencing scheme on decoding performance of LFP-based brain-machine interface. <em>bioRxiv Preprint. </em>2020.05.03.075218 <a href="https://doi.org/10.1101/2020.05.03.075218">doi:10.1101/2020.05.03.075218</a></li> <li>Ahmadi, N., Constandinou, T. & Bouganis, C. (2020). Inferring entire spiking activity from local field potentials. <em>Scientific reports.</em> 11. <a href="https://doi.org/10.1038/s41598-021-98021-9">doi:10.1038/s41598-021-98021-9</a></li> <li>Sachdeva, P. S, Livezey, J. A, Dougherty, M. E., Gu, B.-M., Berke, J. D, & Bouchard, K. E. (2020). Accurate Inference in Parametric Models Reshapes Neuroscientific Interpretation and Improves Data-driven Discovery. <em>bioRxiv Preprint.</em> 2020.04.10.036244. <a href="https://doi.org/10.1101/2020.04.10.036244">doi:10.1101/2020.04.10.036244</a></li> <li>Pei, F., Ye, J., Zoltowski, D., Wu, A., Chowdhury, R. H., Sohn, H., O'Doherty, J. E., Shenoy, K. V., Kaufman, M. T., Churchland, M., Jazayeri, M., Miller, L. E., Pillow, J., Park, I. M., Dyer, E. L., & Pandarinath, C. (2021). Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. <em>arXiv Preprint.</em> <a href="http://arxiv.org/abs/2109.04463">arXiv:2109.04463</a></li> <li>Jensen, K. T., Kao, T.-C., Stone, J. T., & Hennequin, G. (2021). Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. <em>bioRxiv Preprint.</em> 2021.06.03.446788. <a href="https://doi.org/10.1101/2021.06.03.446788">doi:10.1101/2021.06.03.44678</a></li> <li>Savolainen, O.W. (2021). The significance of neural inter-frequency power correlations. <em>Sci. Rep.</em> 11, 23190. <a href="https://doi.org/10.1038/s41598-021-02277-0">doi:10.1038/s41598-021-02277-0</a></li> <li>Schimel, M., Kao, T.-C., Jensen, K.T., & Hennequin, G. (2021). iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. <em>bioRxiv Preprint.</em> 2021.10.07.463540. <a href="https://doi.org/10.1101/2021.10.07.463540">doi:10.1101/2021.10.07.463540</a></li> <li>Li, Y., Qi, Y., Wang, Y., Wang, Y., Xu, K., & Pan, G. (2021). Robust neural decoding by kernel regression with Siamese representation learning. <em>J Neural Eng. </em>18(5): 056062. <a href="http://doi.org/10.1088/1741-2552/ac2c4e">doi:10.1088/1741-2552/ac2c4e</a></li> <li>Savolainen, O. W. (2021). The Significance of Neural Inter-Frequency Correlations. <em>Research Square Preprint (v1)</em>. <a href="https://doi.org/10.21203/rs.3.rs-329644/v1">doi:10.21203/rs.3.rs-329644/v1</a></li> <li>Sani, O. G., Pesaran, B., & Shanechi., M. M. (2021). Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks. <em>bioRxiv Preprint.</em> 2021.09.03.458628. <a href="https://doi.org/10.1101/2021.09.03.458628">doi:10.1101/2021.09.03.458628</a></li> <li>Yang, S.-H., Huang, J.-W., Huang, C.-J., Chiu, P.-H., Lai, H.-Y., & Chen, Y.-Y. (2021). Selection of Essential Neural Activity Timesteps for Intracortical Brain–Computer Interface Based on Recurrent Neural Network. <em>Sensors</em>. 21(19): 6372. <a href="https://doi.org/10.3390/s21196372">doi:10.3390/s21196372</a></li> <li>Sachdeva, P. S., Livezey, J. A., Dougherty, M. E., Gu, B.-M., Berke, J. D., & Bouchard, K. E. (2021). Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation. <em>Journal of Neuroscience Methods. </em>358: 109195. <a href="http://doi.org/10.1016/j.jneumeth.2021.109195">doi:10.1016/j.jneumeth.2021.109195</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis. C.-S. (2021). Impact of referencing scheme on decoding performance of LFP-based brain-machine interface. <em>J Neural Eng. </em>18(1): 016028. <a href="https://doi.org/10.1088/1741-2552/abce3c">doi:10.1088/1741-2552/abce3c</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2021). Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning. <em>J. Neural Eng.</em> 18(2): 026011. <a href="https://doi.org/10.1088/1741-2552/abde8a">doi:10.1088/1741-2552/abde8a</a></li> <li>Keshtkaran, M. R., Sedler, A. R., Chowdhury, R. H., Tandon, R., Basrai, D., Nguyen, S. L, Sohn, H., Jazayeri, M., Miller, L. E., & Pandarinath, C. (2021). A large-scale neural network training framework for generalized estimation of single-trial population dynamics. <em>bioRxiv Preprint</em>. 2021.01.13.426570. <a href="https://doi.org/10.1101/2021.01.13.426570">doi:10.1101/2021.01.13.426570</a></li> <li>Qi, Y., Zhu, X., Xu, K., Ren, F., Jiang, H., Zhu, J., Zhang, J., Pan, G., & Wang, Y. (2022). Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface. <em>IEEE Transactions on Biomedical Engineering.</em> 69(12): 3825-3835. <a href="https://doi.org/10.1109/TBME.2022.3182588">doi:10.1109/TBME.2022.3182588</a></li> <li>Keshtkaran, M. R., Sedler, A. R., Chowdhury, R. H., Tandon, R., Basrai, D., Nguyen, S. L., Sohn, H., Jazayeri, M., Miller, L. E., & Pandarinath, C. (2022). A large-scale neural network training framework for generalized estimation of single-trial population dynamics. <em>Nat Methods.</em> 19, 1572-1577. <a href="https://doi.org/10.1038/s41592-022-01675-0">doi:10.1038/s41592-022-01675-0</a></li> <li>Savolainen, O. W. (2022). Hardware-efficient data compression in wireless intracortical brain-machine interfaces. <em>PhD Dissertation</em>. <a href="https://doi.org/10.25560/105363">doi:10.25560/105363</a></li> <li>Savolainen, O. W., Zhang, Z., Feng, P. & Constandinou, T. G. (2022). Hardware-Efficient Compression of Neural Multi-Unit Activity. <em>bioRxiv Preprint</em>. 2022.03.25.485863 <a href="https://doi.org/10.1101/2022.03.25.485863">doi:10.1101/2022.03.25.485863</a></li> <li>Savolainen, O. W., Zhang, Z. & Constandinou, T. G. (2022). Ultra low power, event-driven data compression of Multi-Unit activity. <em>bioRxiv Preprint</em>. 2022.11.24.517853 <a href="https://doi.org/10.1101/2022.11.24.517853">doi:10.1101/2022.11.24.517853</a></li> <li>Meng, R., Luo, T. & Bouchard, K. (2022). Compressed Predictive Information Coding. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/2203.02051">arXiv:2203.02051</a></li> <li>Li, Y., Zhu, X., Qi, Y. & Wang, Y. (2022). Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals. <em>bioRxiv Preprint</em>. <a href="https://doi.org/10.1101/2022.11.13.515644">doi:10.1101/2022.11.13.515644</a></li> <li>Ahmadi, N., Adiono, T., Purwarianti, A., Constandinou, T. G. & Bouganis, C.-S. (2022). Improved spike-based brain-machine interface using Bayesian adaptive kernel smoother and deep learning. <em>IEEE Access.</em> 10: 29341-29356. <a href="https://doi.org/10.1109/access.2022.3159225">doi:10.1109/access.2022.3159225</a></li> <li>Zhu, X., Qi, Y., Pan, G., Wang, Y. (2022). <a href="https://papers.neurips.cc/paper_files/paper/2022/hash/8dcc306a2522c60a78f047ab8739e631-Abstract-Conference.html">Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding</a>. <em>Advances in Neural Information Processing Systems (NeurIPS) 35.</em></li> <li>Savolainen, O. W., Zhang, Z., Feng, P. & Constandinou, T. G. (2022). Hardware-Efficient Compression of Neural Multi-Unit Activity. <em>IEEE Access</em>. 10: 117515-117529. <a href="https://doi.org/10.1109/access.2022.3219441">doi:10.1109/access.2022.3219441</a></li> <li>Qi, Y., Zhu, X., Xu, K., Ren, F., Jiang, H., Zhu, J., Zhang, J., Pan, G., & Wang, Y. (2022). Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface. <em>arXiv Preprint. </em><a href="https://arxiv.org/abs/2204.11840"><em>arXiv:2204.11840</em></a></li> <li>Valencia, D., Mercier, P. P, & Alimohammad, A. (2022). <em>In vivo</em> neural spike detection with adaptive noise estimation. <em>J Neural Eng.</em> 19: 046018. <a href="https://doi.org/10.1088/1741-2552/ac8077">doi:10.1088/1741-2552/ac8077</a></li> <li>Zhang, Z., Feng, P., Oprea, A. & Constandinou, T. G. (2023). Calibration-free and hardware-efficient neural spike detection for brain machine interfaces. <em>IEEE transactions on biomedical circuits and systems.</em> 17(4): 725-740. <a href="https://doi.org/10.1109/TBCAS.2023.3278531">doi:10.1109/TBCAS.2023.3278531</a></li> <li>Biyan, Z., Sun, P. & Basu, A. (2023). Combining SNNs with filtering for efficient neural decoding in implantable brain-machine interfaces. <em>Neuromorphic Computing and Engineering.</em> 5. <a href="https://doi.org/10.1088/2634-4386/adba82">doi:10.1088/2634-4386/adba82</a></li> <li>Zhang, Z. (2023). Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces. <em>PhD Dissertation. </em><a href="https://doi.org/10.25560/108113">doi:10.25560/108113</a></li> <li>Zhou, B., Sun, P. V. & Basu, A. (2023). Combining SNNs with filtering for efficient neural decoding in implantable brain-machine interfaces. <em>arXiv Preprint</em>. arXiv:XXXX</li> <li>Song, C. Y. & Shanechi, M. M. (2023). Unsupervised learning of stationary and switching dynamical system models from Poisson observations. <em>Journal of neural engineering.</em> 20(6). doi:10.1088/1741-2552/ad038d</li> <li>Bono, M. (2023). Time robustness of deep learning models for real-time neural decoding of arm movement. <em>PhD Dissertation.</em> doi:XXXX</li> <li>Azabou, M., Arora, V., Ganesh, V., Mao, X., Nachimuthu, S., Mendelson, M. J., Richards, B., Perich, M. G., Lajoie, G. & Dyer, E. L. (2023). A unified, scalable framework for neural population decoding. <em>arXiv Preprint. </em>arXiv:XXXX</li> <li>Yik, J., Berghe, K., Blanken, D. d., Bouhadjar, Y., Fabre, M., Hueber, P., Ke, W., Khoei, M. A., Kleyko, D., Pacik-Nelson, N., Pierro, A., Stratmann, P., Sun, P. V., Tang, G., Wang, S., Zhou, B., Ahmed, S. H., Joseph, G. V., Leto, B., Micheli, A., Mishra, A. K., Lenz, G., Sun, T., Ahmed, Z., Akl, M., Anderson, B., Andreou, A. G., Bartolozzi, C., Basu, A., Bogdan, P., Bohte, S., Buckley, S., Cauwenberghs, G., Chicca, E., Corradi, F., Croon, G., Danielescu, A., Daram, A., Davies, M., Demirag, Y., Eshraghian, J., Fischer, T., Forest, J., Fra, V., Furber, S., Furlong, P. M., Gilpin, W., Gilra, A., Gonzalez, H. A., Indiveri, G., Joshi, S., Karia, V., Khacef, L., Knight, J. C., Kriener, L., Kubendran, R., Kudithipudi, D., Liu, S., Liu, Y., Ma, H., Manohar, R., Margarit-Taulé, J. M., Mayr, C., Michmizos, K., Muir, D. R., Neftci, E., Nowotny, T., Ottati, F., Ozcelikkale, A., Panda, P., Park, J., Payvand, M., Pehle, C., Petrovici, M. A., Posch, C., Renner, A., Sandamirskaya, Y., Schaefer, C. J. S., Schaik, A., Schemmel, J., Schmidgall, S., Schuman, C., Seo, J., Sheik, S., Shrestha, S. B., Sifalakis, M., Sironi, A., Stewart, K., Stewart, M., Stewart, T. C., Timcheck, J., Tömen, N., Urgese, G., Verhelst, M., Vineyard, C. M., Vogginger, B., Yousefzadeh, A., Zohora, F. T., Frenkel, C. & Reddi, V. J. (2023). NeuroBench: A framework for benchmarking neuromorphic computing algorithms and systems. <em>arXiv Preprint.</em> arXiv:XXXX</li> <li>Zhang, Z. & Constandinou, T. G. (2023). Firing-rate-modulated spike detection and neural decoding co-design. <em>Journal of neural engineering.</em> 20(3). doi:10.1088/1741-2552/accece</li> <li>Ye, J., Collinger, J. L., Wehbe, L., & Gaunt, R. (2023). Neural Data Transformer 2: Multi-Context Pretraining for Neural Spiking Activity. <em>bioRxiv Preprint</em>. 2023.09.18.558113. <a href="https://doi.org/10.1101/2023.09.18.558113">doi:10.1101/2023.09.18.558113</a></li> <li>Abbaspourazad, H., Erturk, E., Pesaran, B. & Shanechi, M. (2023). Dynamical flexible inference of nonlinear latent structures in neural population activity. bioRxiv Preprint. doi:XXXX</li> <li>Asahina, T., Shimba, K., Kotani, K. & Jimbo, Y. (2023). Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies. J<em>ournal of neuroscience methods.</em> 385(109764): 109764. doi:10.1016/j.jneumeth.2022.109764</li> <li>Meghanath, G., Jimenez, B. & Makin, J. G. (2023). Inferring population dynamics in macaque cortex. <em>Journal of neural engineering. </em>20(5). doi:10.1088/1741-2552/ad0651</li> <li>Abbaspourazad, H., Erturk, E., Pesaran, B. & Shanechi, M. M. (2024). Dynamical flexible inference of nonlinear latent factors and structures in neural population activity. <em>Nature biomedical engineering. </em>8(1): 85-108. doi:10.1038/s41551-023-01106-1</li> <li>Valencia, D. (2024). Towards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation. <em>PhD Dissertation.</em> doi:XXXX</li> <li>Vasilache, A., Krausse, J., Knobloch, K. & Becker, J. (2024). Hybrid spiking neural networks for low-power intra-cortical brain-machine interfaces. <em>arXiv Preprint.</em> arXiv:XXXX</li> <li>Weng, Y., Qi, Y., Wang, Y. & Pan, G. (2024). Neuromorphic model-based neural decoders for brain-computer interfaces: a comparative study. doi:10.1109/biocas61083.2024.10798332</li> <li>Martis, L., Leone, G., Raffo, L. & Meloni, P. (2024). Low-power FPGA-based spiking neural networks for real-time decoding of intracortical neural activity. <em>IEEE sensors journal.</em> 24(24): 42448-42459. doi:10.1109/jsen.2024.3487021</li> <li>Oganesian, L. L., Sani, O. G. & Shanechi, M. (2024). Spectral learning of shared dynamics between generalized-linear processes. <em>Neural Information Processing Systems. </em>37: 89150-89183.</li> <li>Tasca, M. (2024). Time-Robust and Energy-Efficient Decoder for Real-Time Neural Decoding of Primary Motor Cortex Activity. <em>PhD Dissertation.</em> doi:XXXX</li> <li>Wang, Y., Wang, Z. & Liu, S. (2024). Leveraging recurrent neural networks for predicting motor movements from primate motor cortex neural recordings. <em>arXiv Preprint. </em>arXiv:XXXX</li> <li>Liu, T., Gygax, J., Rossbroich, J., Chua, Y., Zhang, S. & Zenke, F. (2024). Decoding finger velocity from cortical spike trains with recurrent spiking neural networks. <em>arXiv Preprint. </em>arXiv:XXX</li> <li>Schulz, A., Vetter, J., Gao, R., Morales, D., Lobato-Rios, V., Ramdya, P., Gonçalves, P. J. & Macke, J. H. (2024). Modeling conditional distributions of neural and behavioral data with masked variational autoencoders. <em>bioRxiv Preprint. </em>doi:10.1101/2024.04.19.590082</li> <li>Sani, O. G., Pesaran, B. & Shanechi, M. M. (2024). Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks. <em>Nature Neuroscience. </em>27(10): 2033-2045. doi:10.1038/s41593-024-01731-2</li> <li>Kumar, A., Frank, L. M. & Bouchard, K. E. (2024). Identifying feedforward and feedback controllable subspaces of neural population dynamics. <em>arXiv Preprint.</em> arXiv:XXXX</li> <li>McCart, J. D., Sedler, A. R., Versteeg, C., Mifsud, D., Rigotti-Thompson, M. & Pandarinath, C. (2024). Diffusion-based generation of neural activity from disentangled latent Codes. <em>arXiv Preprint. </em>arXiv:XXXX</li> <li>Bouchard, K. & Kumar, A. (2024). Feedback controllability is a normative theory of neural population dynamics. <em>Research Square. </em>doi:10.21203/rs.3.rs-4102129/v1</li> <li>Yang, S., Huang, C. & Huang, J. (2024). Increasing robustness of intracortical brain-computer interfaces for recording condition changes via data augmentation. <em>Computer methods and programs in biomedicine. </em>251(108208): 108208. doi:10.1016/j.cmpb.2024.108208</li> <li>Wang, C., Yin, M., Liang, F. & Wang, X. (2024). A robust and high accurate method for hand kinematics decoding from neural populations. doi:10.1007/978-981-99-8546-3\_20</li> <li>Mohan, V., Tay, W. P. & Basu, A. (2025). Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface. <em>Neuromorphic Computing and Engineering.</em> 5(1): 014004. doi:10.1088/2634-4386/adad10</li> <li>Vahidi, P., Sani, O. G. & Shanechi, M. (2025). BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data. International Conference on Learning Representations.</li> <li>Leone, G., Martis, L., Raffo, L. & Meloni, P. (2025). Enabling SNN-based near-MEA neural decoding with channel selection: An open-HW approach. doi:10.23919/date64628.2025.10993220</li> <li>Mohan, V., Zhou, B., Wang, Z., Bharath, A., Drakakis, E. & Basu, A. (2025). Architectural exploration of hybrid neural decoders for neuromorphic implantable BMI. <em>arXiv Preprint. </em>arXiv:XXXX</li> <li>Yik, J., Berghe, K., Blanken, D., Bouhadjar, Y., Fabre, M., Hueber, P., Ke, W., Khoei, M. A., Kleyko, D., Pacik-Nelson, N., Pierro, A., Stratmann, P., Sun, P. V., Tang, G., Wang, S., Zhou, B., Ahmed, S. H., Vathakkattil Joseph, G., Leto, B., Micheli, A., Mishra, A. K., Lenz, G., Sun, T., Ahmed, Z., Akl, M., Anderson, B., Andreou, A. G., Bartolozzi, C., Basu, A., Bogdan, P., Bohte, S., Buckley, S., Cauwenberghs, G., Chicca, E., Corradi, F., Croon, G., Danielescu, A., Daram, A., Davies, M., Demirag, Y., Eshraghian, J., Fischer, T., Forest, J., Fra, V., Furber, S., Furlong, P. M., Gilpin, W., Gilra, A., Gonzalez, H. A., Indiveri, G., Joshi, S., Karia, V., Khacef, L., Knight, J. C., Kriener, L., Kubendran, R., Kudithipudi, D., Liu, S., Liu, Y., Ma, H., Manohar, R., Margarit-Taulé, J. M., Mayr, C., Michmizos, K., Muir, D. R., Neftci, E., Nowotny, T., Ottati, F., Ozcelikkale, A., Panda, P., Park, J., Payvand, M., Pehle, C., Petrovici, M. A., Posch, C., Renner, A., Sandamirskaya, Y., Schaefer, C. J. S., Schaik, A., Schemmel, J., Schmidgall, S., Schuman, C., Seo, J., Sheik, S., Shrestha, S. B., Sifalakis, M., Sironi, A., Stewart, K., Stewart, M., Stewart, T. C., Timcheck, J., Tömen, N., Urgese, G., Verhelst, M., Vineyard, C. M., Vogginger, B., Yousefzadeh, A., Zohora, F. T., Frenkel, C. & Reddi, V. J. (2025). The neurobench framework for benchmarking neuromorphic computing algorithms and systems. <em>Nature Communications. </em>16(1): 1545. doi:10.1038/s41467-025-56739-4</li> <li>Zheng, J., Li, Y., Chen, L., Wang, F., Gu, B., Sun, Q., Gao, X. & Zhou, F. (2025). Effects of packet loss on neural decoding effectiveness in wireless transmission. <em>Brain Sciences.</em> 15(3): 221. doi:10.3390/brainsci15030221</li> </ol> <p><strong>History.</strong></p> <ul> <li>Version 2 - added CSV of results from Makin et al.</li> <li>Version 1 - initial release.</li> </ul>
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Data set for "Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice"
<p>Data set for: Auffret M, Ravano VL, Rossi GMC, Hankov N, Petersen MFA, Petersen CCH (2017) Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice. Neuroscience, http://dx.doi.org/10.1016/j.neuroscience.2017.04.004</p> <p>There are 9 files in this data upload:</p> <ol> <li>'2017_Auffret_Neuroscience.pdf' - this is a pdf version of the online publication.</li> <li>'Auffret_data.mat' - this is a Matlab data structure, which contains all the data for the publication.</li> <li>'Auffret_data.npy' - this is a Python data structure, which contains all the data for the publication. The Python data was generated from 'Auffret_data.mat' by 'DataViewer.py'.</li> <li>'Auffret_data.xlsx' - this is an Excel file, which contains all the data for the publication. This Excel file was generated from 'Auffret_data.mat'.</li> <li>'DataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'DataViewer.m'.</li> <li>'DataViewer.m' - this is a Matlab Code, which displays the data contained in 'Auffret_data.mat'.</li> <li>'DataViewer.py' - this is a Python Code, which generates 'Auffret_data.npy' from 'Auffret_data.mat', and displays an example trial.</li> <li>'FigureViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'FigureViewer.m'.</li> <li>'FigureViewer.m' - this is a Matlab Code, which analyses the data in 'Auffret_data.mat', and displays the results in the same way as the published figures (Auffret et al., 2017).</li> </ol>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Human kidney cortex CODEX reference dataset 1
<p>CODEX image stack of human kidney cortex stained with markers as indicated in CODEX_antibody_list_010621.csv and imaged in the order given in CODEX_channel_index_010621.csv. Tissue preparation and analysis as described <a href="https://www.biorxiv.org/content/10.1101/2021.12.27.474025v1">here.</a></p>
ScienceDex guides
Understand access before you commit
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.