Find research datasets worth reusing

opencc-by-4.0May 2017View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology

General Description. This dataset consists of: <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> 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). 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. Possible uses. 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. Variable names. Each file contains data in the following format. In the below, n refers to the number of recording channels, u refers to the number of sorted units, and k refers to the number of samples. <ul> <li>chan_names - n x 1 <ul> <li>A cell array of channel identifier strings, e.g. "M1 001".</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 or 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: \(\begin{pmatrix} 0 & 0 \\ -10 & 0 \\ 0 & -10 \end{pmatrix}\) 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 (u1) is the "unsorted" unit, meaning it contains the threshold crossings which remained after the spikes on that channel were sorted into other units (u2, u3, etc.) For some sessions spikes were sorted into up to 2 units (i.e. u=3); for others, 4 units (u=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> Decoder Results. 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 refh_results.csv, containing the following columns: <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> Videos. 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: <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> Supplements. The raw broadband neural recordings that the spike trains in this dataset were extracted from are available for the following sessions: <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> Contact  Information. 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>. Citation. @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} } Publications making use of this dataset. <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. J Neural Eng. 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. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 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. arXiv Preprint. <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, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 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. 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 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. 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 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. bioRxiv Preprint. 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> Advances in Neural Information Processing Systems (NeurIPS) 32.</li> <li>Clark, D. G., Livezey, J. A., & Bouchard, K. E. (2019). Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. arXiv Preprint. <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. IEEE Transactions on Biomedical Circuits and Systems. 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. arXiv Preprint. <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. Proceeding of the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS). 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. arXiv Preprint. <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. bioRxiv Preprint. 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. TechRxiv Preprint. <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. bioRxiv Preprint. 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. bioRxiv Preprint. 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. Scientific reports. 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. bioRxiv Preprint. 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. arXiv Preprint. <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. bioRxiv Preprint. 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. Sci. Rep. 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. bioRxiv Preprint. 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. J Neural Eng. 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. Research Square Preprint (v1). <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. bioRxiv Preprint. 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. Sensors. 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. Journal of Neuroscience Methods. 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. J Neural Eng. 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. J. Neural Eng. 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. bioRxiv Preprint. 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. IEEE Transactions on Biomedical Engineering. 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. Nat Methods. 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. PhD Dissertation. <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. bioRxiv Preprint. 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. bioRxiv Preprint. 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. arXiv Preprint. <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. bioRxiv Preprint. <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. IEEE Access. 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>. Advances in Neural Information Processing Systems (NeurIPS) 35.</li> <li>Savolainen, O. W., Zhang, Z., Feng, P. & Constandinou, T. G. (2022). Hardware-Efficient Compression of Neural Multi-Unit Activity. IEEE Access. 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. arXiv Preprint. <a href="https://arxiv.org/abs/2204.11840">arXiv:2204.11840</a></li> <li>Valencia, D., Mercier, P. P, & Alimohammad, A. (2022). In vivo neural spike detection with adaptive noise estimation. J Neural Eng. 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. IEEE transactions on biomedical circuits and systems. 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. Neuromorphic Computing and Engineering. 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. PhD Dissertation. <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. arXiv Preprint. arXiv:XXXX</li> <li>Song, C. Y. & Shanechi, M. M. (2023). Unsupervised learning of stationary and switching dynamical system models from Poisson observations. Journal of neural engineering. 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. PhD Dissertation. 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. arXiv Preprint. 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. arXiv Preprint. arXiv:XXXX</li> <li>Zhang, Z. & Constandinou, T. G. (2023). Firing-rate-modulated spike detection and neural decoding co-design. Journal of neural engineering. 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. bioRxiv Preprint. 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. Journal of neuroscience methods. 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. Journal of neural engineering. 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. Nature biomedical engineering. 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. PhD Dissertation. 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. arXiv Preprint. 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. IEEE sensors journal. 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. Neural Information Processing Systems. 37: 89150-89183.</li> <li>Tasca, M. (2024). Time-Robust and Energy-Efficient Decoder for Real-Time Neural Decoding of Primary Motor Cortex Activity. PhD Dissertation. 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. arXiv Preprint. 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. arXiv Preprint. 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. bioRxiv Preprint. 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. Nature Neuroscience. 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. arXiv Preprint. 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. arXiv Preprint. arXiv:XXXX</li> <li>Bouchard, K. & Kumar, A. (2024). Feedback controllability is a normative theory of neural population dynamics. Research Square. 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. Computer methods and programs in biomedicine. 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. Neuromorphic Computing and Engineering. 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. arXiv Preprint. 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. Nature Communications. 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. Brain Sciences. 15(3): 221. doi:10.3390/brainsci15030221</li> </ol> History. <ul> <li>Version 2 - added CSV of results from Makin et al.</li> <li>Version 1 - initial release.</li> </ul>

opencc-by-4.0Jan 2018View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170131_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170131_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information. History <ul> <li>Version 2 - corrects a error with the electrode mapping.</li> <li>Version 1 - initial release.</li> </ul>

opencc-by-4.0Aug 2019View details →

Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"

Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008 There are 2 files in this upload: 1. The file named "2019_Mayrhofer_Neuron.pdf" is the Open Access pdf file of the manuscript published in Neuron. 2. The file named "Mayrhofer_data_code.zip" (~20 GB) is a zipped version of a folder "Mayrhofer_data_code" (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named "MatlabCode", and the specific code for generating each figure panel is in a sub-subfolder named "Figures_tjM1_paper". When running the code, you need to set the Matlab file path to be "Mayrhofer_data_code". In addition, you should add the folder "Mayrhofer_data_code" with subfolders in Matlab "Set Path". The figures will be saved in a subfolder named "Figures". Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.

opencc-by-4.0Oct 2021View details →

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

Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049 There are 2 files in this upload: 1. The file named "2021_Sippy_FUNCTION.pdf" is the Open Access pdf of the online publication in FUNCTION. 2. The file named "Sippy_data_code.zip" (~5 GB) is a zipped version of a folder ‘Sippy_data_code’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Sippy_data_code’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’. The subfolder ‘Data’ contains the data structure ‘Data.mat’ to be analyzed, as well as a Matlab file called ‘p_value_colormap.mat’ used to plot the p value color bars in some figures. The subfolder ‘Functions’ contains functions called by the main codes. The subfolder ‘Codes’ contains the following codes: ‘AnalyzeDataStructure.m’: computes the results and saves them as a new data structure called ‘Analyzed_Data’, in the subfolder ‘Results’. ‘Figure_1.m’: computes and plots the results for the panels D, E and F of Figure 1. ‘Figure_2.m’: computes and plots the results for the panels D-G and I-K of Figure 2. ‘Figure_3.m’: computes and plots the results for the panels A-F of Figure 3. ‘SuppFigure_2.m’: computes and plots the results for the panels B, D and F of Supplementary Figure 2. ‘SuppFigure_3.m’: computes and plots the results for the panels A-D of Supplementary Figure 3. ‘SuppFigure_4.m': computes and plots the results for the panels A-C of Supplementary Figure 4.   The data structures contain the following fields: ‘Mouse_Name’: name of the mouse. ‘Mouse_RecordingDate’: date of recording (YMD). ‘Mouse_DateOfBirth’: date of birth of the mouse (YMD). ‘Mouse_Sex’: sex of the mouse (F or M). ‘Mouse_Genotype’: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice. ‘Mouse_Level’: Training level (NAÏVE or EXPERT). ‘Cell_Counter’: cell recorded in a given mouse. ‘Cell_Type’: type of the recorded cell (dSPN, iSPN or TAN). ‘Cell_TargetedBrainArea’: Brain area targeted (DLS). ‘Cell_Recovered’: Indicate cells that have been labelled and anatomically recovered (TRUE). ‘Cell_Coordinates’: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal) ‘Cell_Fluorescence’: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf Mouse_Genotype) with Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN (cf Cell_Type). ‘Sweep_Counter’: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s). ‘Sweep_Type’: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task). ‘Sweep_MembranePotential’: membrane potential recording (mV) after cutting of the APs. ‘Sweep_CurrentInjected’: current injected into the cell (pA). ‘Sweep_PiezoLick’: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps. ‘Sweep_Trial’: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps. ‘Sweep_WhiskerStim’: voltage command triggering the onset of each whisker stimulus in behavior sweeps. ‘Sweep_Valve’: voltage command triggering the opening of the valve delivering the reward in Hit trials. ‘Sweep_SamplingRate’: sampling rate (sample.s-1) of the recorded signals for each sweep. ‘Sweep_TimeStamp’: time at the beginning of the recorded sweep (H/min/s). ‘Sweep_Reward’: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps. ‘Sweep_APThresh’: Threshold (V) used to detect action potentials (AP) during current injection.

dryad40/100

Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

opencc-zeroFeb 2020View details →

opencc-by-4.0Nov 2024View details →

Rapid sensorimotor adaptation to auditory midbrain silencing in free-flying bats

This dataset accompanies the manuscript entitled "Rapid sensorimotor adaptation to auditory midbrain silencing in free-flying bats". The structures contain the preprocessed data for each bat (individual structures for vocal and flight trajectory, or an overall structure for behavior).  The original code that allows for the figure generation associated with this manuscript is included.   The data for the audiograms and ABRs is also provided in csv format.

opencc-by-4.0Jul 2022View details →

The impact of cerebellar transcranial direct current stimulation (tDCS) on sensorimotor and inter-sensory temporal recalibration

Data related to the study "The impact of cerebellar transcranial direct current stimulation (tDCS) on sensorimotor and inter-sensory temporal recalibration".

opencc-by-4.0May 2024View details →

Kinetic features dictate sensorimotor alignment in the superior colliculus.

Tetrode recordings dataset from González-Rueda et al. “Kinetic features dictate sensorimotor alignment in the superior colliculus”, Nature 2024 (https://doi.org/10.1038/s41586-024-07619-2) This subset of data corresponds to 5 C57BL6 male adult mice recorded both freely moving during an open field foraging task ("Open field") and head restrained during two visual stimulation tasks ("Visual Stimulation"). Neurons were recorded in both conditions across the superior colliculus using tetrodes in order to determine the sensorimotor alignment between static/kinetic visual features and egocentric head rotations. Detailled explanation of data organisation and content can be found in the file Guide.pdf.

opencc-by-4.0Jan 2018View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170127_03

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170127_03". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

opencc-by-4.0Jan 2018View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170124_01

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170124_01". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

opencc-by-4.0Feb 2018View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170123_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170123_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

opencc-by-4.0Jun 2018View details →

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161220_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161220_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161207_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161207_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161212_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161212_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161206_02

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161206_02". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161026_03

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161026_03". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.

Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20161027_03

This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> . General description. These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161027_03". Filtering. The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate. File format. The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification. Datasets. A few of the relevant dataset paths are listed here for convenience. In the below, n refers to the number of recording channels and k refers to the number of samples. <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.