DOI: 10.5281/zenodo.788569
title: Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology

description: General Description. This dataset consists of:



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,

The x,y position of the fingertip of the reaching hand and the x,y position of reaching targets (both sampled at 250 Hz).


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.



chan_names - n x 1



A cell array of channel identifier strings, e.g. "M1 001".



cursor_pos - k x 2



The position of the cursor in Cartesian coordinates (x, y), mm.



finger_pos - k x 3 or k x 6



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).



target_pos - k x 2



The position of the target in Cartesian coordinates (x, y), mm.



t - k x 1



The timestamp corresponding to each sample of the cursor_pos, finger_pos, and target_pos, seconds.



spikes - n x u



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).



wf - n x u



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.




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:



session - a session identifier, e.g. "indy_20160407_02"

monkey - one of, "indy" or "loco"

num_neurons - total number of features used in the decoder

num_training_samples - number of samples (at the specified bin-width) used to train the decoder (sequential, from file start)

num_testing_samples - number of samples used to evaluate the decoder (sequential, until file end)

kinematic_axis - one of, "posx", "posy", "velx", "vely", "accx" or "accy"

bin_width - one of, "16", "32", "64" or "128"

decoder - one of, "regression", "KF_observed", "KF_static", "KF_dynamic", "UKF", "rEFH_static" or "rEFH_dynamic"

rsq - coefficient of determination, R2

snr - Signal to noise ratio, SNR := -10 log10(1 - R2)


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:



indy_20160921_01

indy_20160930_02

indy_20160930_05

indy_20161005_06

indy_20161006_02

indy_20161007_02

indy_20161011_03

indy_20161013_03

indy_20161014_04

indy_20161017_02


Supplements. The raw broadband neural recordings that the spike trains in this dataset were extracted from are available for the following sessions:



indy_20160622_01: doi:10.5281/zenodo.1488440

indy_20160624_03: doi:10.5281/zenodo.1486147

indy_20160627_01: doi:10.5281/zenodo.1484824

indy_20160630_01: doi:10.5281/zenodo.1473703

indy_20160915_01: doi:10.5281/zenodo.1467953

indy_20160916_01: doi:10.5281/zenodo.1467050

indy_20160921_01: doi:10.5281/zenodo.1451793

indy_20160927_04: doi:10.5281/zenodo.1433942

indy_20160927_06: doi:10.5281/zenodo.1432818

indy_20160930_02: doi:10.5281/zenodo.1421880

indy_20160930_05: doi:10.5281/zenodo.1421310

indy_20161005_06: doi:10.5281/zenodo.1419774

indy_20161006_02: doi:10.5281/zenodo.1419172

indy_20161007_02: doi:10.5281/zenodo.1413592

indy_20161011_03: doi:10.5281/zenodo.1412635

indy_20161013_03: doi:10.5281/zenodo.1412094

indy_20161014_04: doi:10.5281/zenodo.1411978

indy_20161017_02: doi:10.5281/zenodo.1411882

indy_20161024_03: doi:10.5281/zenodo.1411474

indy_20161025_04: doi:10.5281/zenodo.1410423

indy_20161026_03: doi:10.5281/zenodo.1321264

indy_20161027_03: doi:10.5281/zenodo.1321256

indy_20161206_02: doi:10.5281/zenodo.1303720

indy_20161207_02: doi:10.5281/zenodo.1302866

indy_20161212_02: doi:10.5281/zenodo.1302832

indy_20161220_02: doi:10.5281/zenodo.1301045

indy_20170123_02: doi:10.5281/zenodo.1167965

indy_20170124_01: doi:10.5281/zenodo.1163026

indy_20170127_03: doi:10.5281/zenodo.1161225

indy_20170131_02: doi:10.5281/zenodo.854733


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.



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. doi:10.1088/1741-2552/aa9e95

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. doi:10.1109/EMBC.2018.8512830

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. arXiv:1808.06992

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. doi:10.1109/EMBC.2019.8856316

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. doi:10.1109/biocas.2019.8919131

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. doi:10.1109/IEEECONF44664.2019.9048891

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. doi:10.1101/772988

Keshtkaran, M. R., & Pandarinath, C. (2019). Enabling hyperparameter optimization in sequential autoencoders for spiking neural data. Advances in Neural Information Processing Systems (NeurIPS) 32.

Clark, D. G., Livezey, J. A., & Bouchard, K. E. (2019). Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. arXiv Preprint. arXiv:1905.09944

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. doi:10.1109/TBCAS.2019.2944486

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. arXiv:1901.00708

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. doi: 10.1109/IPDPS47924.2020.00036

Bose, S. K., Acharya, J. & Basu, A. (2020). Is my neural network neuromorphic? Taxonomy, recent trends and future directions in neuromorphic engineering. arXiv Preprint. arXiv:2002.11945

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. doi:10.1101/2020.05.02.074104

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. doi:10.36227/techrxiv.12383600.v1

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 doi:10.1101/2020.05.07.083063

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 doi:10.1101/2020.05.03.075218

Ahmadi, N., Constandinou, T. & Bouganis, C. (2020). Inferring entire spiking activity from local field potentials. Scientific reports. 11. doi:10.1038/s41598-021-98021-9

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. doi:10.1101/2020.04.10.036244

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. arXiv:2109.04463

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. doi:10.1101/2021.06.03.44678

Savolainen, O.W. (2021). The significance of neural inter-frequency power correlations. Sci. Rep. 11, 23190. doi:10.1038/s41598-021-02277-0

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. doi:10.1101/2021.10.07.463540

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. doi:10.1088/1741-2552/ac2c4e

Savolainen, O. W. (2021). The Significance of Neural Inter-Frequency Correlations. Research Square Preprint (v1). doi:10.21203/rs.3.rs-329644/v1

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. doi:10.1101/2021.09.03.458628

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. doi:10.3390/s21196372

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. doi:10.1016/j.jneumeth.2021.109195

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. doi:10.1088/1741-2552/abce3c

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. doi:10.1088/1741-2552/abde8a

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. doi:10.1101/2021.01.13.426570

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. doi:10.1109/TBME.2022.3182588

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. doi:10.1038/s41592-022-01675-0

Savolainen, O. W. (2022). Hardware-efficient data compression in wireless intracortical brain-machine interfaces. PhD Dissertation. doi:10.25560/105363

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 doi:10.1101/2022.03.25.485863

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 doi:10.1101/2022.11.24.517853

Meng, R., Luo, T. & Bouchard, K. (2022). Compressed Predictive Information Coding. arXiv Preprint. arXiv:2203.02051

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. doi:10.1101/2022.11.13.515644

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. doi:10.1109/access.2022.3159225

Zhu, X., Qi, Y., Pan, G., Wang, Y. (2022). Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding. Advances in Neural Information Processing Systems (NeurIPS) 35.

Savolainen, O. W., Zhang, Z., Feng, P. & Constandinou, T. G. (2022). Hardware-Efficient Compression of Neural Multi-Unit Activity. IEEE Access. 10: 117515-117529. doi:10.1109/access.2022.3219441

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. arXiv:2204.11840

Valencia, D., Mercier, P. P, & Alimohammad, A. (2022). In vivo neural spike detection with adaptive noise estimation. J Neural Eng. 19: 046018. doi:10.1088/1741-2552/ac8077

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. doi:10.1109/TBCAS.2023.3278531

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. doi:10.1088/2634-4386/adba82

Zhang, Z. (2023). Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces. PhD Dissertation. doi:10.25560/108113

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

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

Bono, M. (2023). Time robustness of deep learning models for real-time neural decoding of arm movement. PhD Dissertation. doi:XXXX

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

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

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

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. doi:10.1101/2023.09.18.558113

Abbaspourazad, H., Erturk, E., Pesaran, B. & Shanechi, M. (2023). Dynamical flexible inference of nonlinear latent structures in neural population activity. bioRxiv Preprint. doi:XXXX

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

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

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

Valencia, D. (2024). Towards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation. PhD Dissertation. doi:XXXX

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

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

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

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.

Tasca, M. (2024). Time-Robust and Energy-Efficient Decoder for Real-Time Neural Decoding of Primary Motor Cortex Activity. PhD Dissertation. doi:XXXX

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

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

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

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

Kumar, A., Frank, L. M. & Bouchard, K. E. (2024). Identifying feedforward and feedback controllable subspaces of neural population dynamics. arXiv Preprint. arXiv:XXXX

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

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History.



Version 2 - added CSV of results from Makin et al.

Version 1 - initial release.
description: This research was supported by the Congressionally Directed Medical Research Program (W81XWH-14-1-0510). JEO was supported by fellowship #2978 from the Paralyzed Veterans of America. JGM was supported by a fellowship from the Swartz Foundation.