A Unified, Scalable Framework for Neural Population Decoding
In preparation
Transcriptomic cell type structures in vivo neuronal activity across multiple time scales
+Contributed equally as co-first authors
Cell Reports, Volume 42, Issue 4, April 2023.
Cite this paper
@article{SCHNEIDER2023112318,
title = {Transcriptomic cell type structures in vivo neuronal activity across multiple timescales},
journal = {Cell Reports},
volume = {42},
number = {4},
pages = {112318},
year = {2023},
issn = {2211-1247},
doi = {https://doi.org/10.1016/j.celrep.2023.112318},
url = {https://www.sciencedirect.com/science/article/pii/S2211124723003297},
author = {Aidan Schneider and Mehdi Azabou and Louis McDougall-Vigier and David F. Parks and Sahara Ensley and Kiran Bhaskaran-Nair and Tomasz Nowakowski and Eva L. Dyer and Keith B. Hengen},
}
APA
Schneider, A., Azabou, M., McDougall-Vigier, L., Parks, D. B., Ensley, S., Bhaskaran-Nair, K., Nowakowski, T., Dyer, E. L. & Hengen, K. B. (2023). Transcriptomic cell type structures in vivo neuronal activity across multiple time scales. Cell Reports, Volume 42, Issue 4, 2023
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
Preprint, Mar 2023
Cite this paper
@misc{azabou2023relax,
doi = {10.48550/ARXIV.2303.08811},
url = {https://arxiv.org/abs/2303.08811},
author = {Azabou, Mehdi and Mendelson, Michael and Ahad, Nauman and Sorokin, Maks and Thakoor, Shantanu and Urzay, Carolina and Dyer, Eva L.},
title = {Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis},
publisher = {arXiv},
year = {2023}
}
APA
Mendelson, M., Azabou, M., Jacob, S., Grissom, N., Darrow, D., Ebitz, B., Herman, A. & Dyer, E. L. (2023). Learning signatures of decision making from many individuals playing the same game. arXiv.
Learning signatures of decision making from many individuals playing the same game
+Contributed equally as co-first authors
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Baltimore, Maryland, April 2023
Cite this paper
@INPROCEEDINGS{10123846,
author={Mendelson, Michael J. and Azabou, Mehdi and Jacob, Suma and Grissom, Nicola and Darrow, David and Ebitz, Becket and Herman, Alexander and Dyer, Eva L.},
booktitle={2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)},
title={Learning signatures of decision making from many individuals playing the same game},
year={2023},
volume={},
number={},
pages={1-5},
doi={10.1109/NER52421.2023.10123846}
}
APA
Mendelson, M., Azabou, M., Jacob, S., Grissom, N., Darrow, D., Ebitz, B., Herman, A. & Dyer, E. L. (2023). Learning signatures of decision making from many individuals playing the same game. 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Baltimore, MD, USA, 2023, pp. 1-5, doi: 10.1109/NER52421.2023.10123846.
Detecting change points in neural population activity with contrastive metric learning
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Baltimore, Maryland, April 2023
Cite this paper
@INPROCEEDINGS{10123821,
author={Urzay, Carolina and Ahad, Nauman and Azabou, Mehdi and Schneider, Aidan and Atamkuri, Geethika and Hengen, Keith B. and Dyer, Eva L.},
booktitle={2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)},
title={Detecting change points in neural population activity with contrastive metric learning},
year={2023},
volume={},
number={},
pages={1-4},
doi={10.1109/NER52421.2023.10123821}
}
APA
Urzay, C., Ahad, N., Azabou, M., Schneider, A., Atmakuri, G., Hengen, K. B., & Dyer, E. L., Detecting change points in neural population activity with contrastive metric learning, 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Baltimore, MD, USA, 2023, pp. 1-4, doi: 10.1109/NER52421.2023.10123821.
2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Neural Information Processing Systems (NeurIPS), 2022
Cite this paper
@misc{https://doi.org/10.48550/arxiv.2206.06131,
doi = {10.48550/ARXIV.2206.06131},
url = {https://arxiv.org/abs/2206.06131},
author = {Liu, Ran and Azabou, Mehdi and Dabagia, Max and Xiao, Jingyun and Dyer, Eva L.},
keywords = {Neurons and Cognition (q-bio.NC), Machine Learning (cs.LG), FOS: Biological sciences, FOS: Biological sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
APA
Liu, R., Azabou, M., Dabagia, M., Xiao, J., & Dyer, E. L. (2022). Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. arXiv preprint arXiv:2206.06131.
Cite this paper
@inproceedings{
thakoor2022largescale,
title={Large-Scale Representation Learning on Graphs via Bootstrapping},
author={Shantanu Thakoor and Corentin Tallec and Mohammad Gheshlaghi Azar and Mehdi Azabou and Eva L Dyer and Remi Munos and Petar Veli{\v{c}}kovi{\'c} and Michal Valko},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=0UXT6PpRpW}
}
APA
Thakoor, S., Tallec, C., Azar, M. G., Azabou, M., Dyer, E. L., Munos, R., Veli{\v{c}}kovi{\'c}, P., & Valko, M. (2021). Large-scale representation learning on graphs via bootstrapping. International Conference on Learning Representations.
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022
Cite this paper
@article{quesadamtneuro,
title={MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction},
author={Quesada, Jorge and Sathidevi, Lakshmi and Liu, Ran and Ahad, Nauman and Jackson, Joy M and Azabou, Mehdi and Xiao, Jingyun and Liding, Chris and Urzay, Carolina and Gray-Roncal, William and Johnson, Erik Christopher, Dyer, Eva L.}
}
APA
Quesada, J., Sathidevi, L., Liu, R., Ahad, N., Jackson, J. M., Azabou, M., Xiao, J. and Liding, C. and Urzay, C. and Gray-Roncal, W. and Johnson, E. C. & Dyer, E. L. MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.
Cite this paper
@article{azabou2022learning,
title={Learning Behavior Representations Through Multi-Timescale Bootstrapping},
author={Azabou, Mehdi and Mendelson, Michael and Sorokin, Maks and Thakoor, Shantanu and Ahad, Nauman and Urzay, Carolina and Dyer, Eva L},
journal={arXiv preprint arXiv:2206.07041},
year={2022}
}
APA
Azabou, M., Mendelson, M., Sorokin, M., Thakoor, S., Ahad, N., Urzay, C., & Dyer, E. L. (2022). Learning Behavior Representations Through Multi-Timescale Bootstrapping. arXiv preprint arXiv:2206.07041.
2021
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Neural Information Processing Systems (NeurIPS), accepted for Oral (1% submissions), 2021
Cite this paper
@inproceedings{NEURIPS2021_58182b82,
author = {Liu, Ran and Azabou, Mehdi and Dabagia, Max and Lin, Chi-Heng and Gheshlaghi Azar, Mohammad and Hengen, Keith and Valko, Michal and Dyer, Eva},
booktitle = {Advances in Neural Information Processing Systems},
editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
pages = {10587--10599},
publisher = {Curran Associates, Inc.},
title = {Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity},
url = {https://proceedings.neurips.cc/paper/2021/file/58182b82110146887c02dbd78719e3d5-Paper.pdf},
volume = {34},
year = {2021}
}
APA
Liu, R., Azabou, M., Dabagia, M., Lin, C. H., Gheshlaghi Azar, M., Hengen, K., Valko, M. & Dyer, E. (2021). Drop, swap, and generate: A self-supervised approach for generating neural activity. Advances in Neural Information Processing Systems, 34, 10587-10599.
Cite this paper
@article{azabou2021mine,
title={Mine your own view: Self-supervised learning through across-sample prediction},
author={Azabou, Mehdi and Azar, Mohammad Gheshlaghi and Liu, Ran and Lin, Chi-Heng and Johnson, Erik C and Bhaskaran-Nair, Kiran and Dabagia, Max and Avila-Pires, Bernardo and Kitchell, Lindsey and Hengen, Keith B and Gray-Roncal, William and Valko, Michal and Dyer, Eva L},
journal={arXiv preprint arXiv:2102.10106},
year={2021}
}
APA
Azabou, M., Azar, M. G., Liu, R., Lin, C. H., Johnson, E. C., Bhaskaran-Nair, K., Dabagia, M., Avila-Pires, B., Kitchell, L., Hengen, K. B., Gray-Roncal, W., Valko, M. & Dyer, E. L. (2021). Mine your own view: Self-supervised learning through across-sample prediction. arXiv preprint arXiv:2102.10106.
Cite this paper
@article{azabouusing,
title={Using self-supervision and augmentations to build insights into neural coding},
author={Azabou, Mehdi and Dabagia, Max and Liu, Ran and Lin, Chi-Heng and Hengen, Keith B and Dyer, Eva L},
journal={NeurIPS 2021 Workshop on Self-supervised Learning: Theory and Practice},
year = {2021}
}
APA
Azabou, M., Dabagia, M., Liu, R., Lin, C. H., Hengen, K. B. & Dyer, E. L. Using self-supervision and augmentations to build insights into neural coding.
Making transport more robust and interpretable by moving data through a small number of anchor points
International Conference on Machine Learning (ICML), 2021
Cite this paper
@InProceedings{lin2021,
title = {Making transport more robust and interpretable by moving data through a small number of anchor points},
author = {Lin, Chi-Heng and Azabou, Mehdi and Dyer, Eva},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {6631--6641},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
}
APA
Lin, C., Azabou, M. & Dyer, E.. (2021). Making transport more robust and interpretable by moving data through a small number of anchor points. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6631-6641 Available from https://proceedings.mlr.press/v139/lin21a.html.
Abstracts
Detecting change points in neural population activity with contrastive metric learning
Conference on Cognitive Computational Neuroscience (CCN), 2022
Cite this paper
@misc{Urzay_Ahad_Azabou_Schneider_Atmakuri_Hengen_Dyer_2022,
title={Detecting change points in neural population activity with contrastive metric learning},
url={http://dx.doi.org/10.32470/CCN.2022.1261-0},
DOI={10.32470/ccn.2022.1261-0},
journal={2022 Conference on Cognitive Computational Neuroscience},
publisher={Cognitive Computational Neuroscience},
author={Urzay, Carolina and Ahad, Nauman and Azabou, Mehdi and Schneider, Aidan and Atmakuri, Geethika and Hengen, Keith B. and Dyer, Eva L.},
year={2022}
}
APA
Urzay, C., Ahad, N., Azabou, M., Schneider, A., Atmakuri, G., Hengen, K. B., & Dyer, E. L. (2022). Detecting change points in neural population activity with contrastive metric learning. In 2022 Conference on Cognitive Computational Neuroscience. https://doi.org/10.32470/ccn.2022.1261-0