Author

Mehdi Azabou

Georgia Institute of Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Neuroscience· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1 Organism:non-human primate· 1

Frequent Co-Authors

Eva L. DyeriD· 3 shared Keith B. HengeniD· 2 shared Max Dabagia· 2 shared Aidan M SchneideriD· 1 shared Chi-Heng Lin· 1 shared David F. ParksiD· 1 shared Jingyun Xiao· 1 shared Kiran Bhaskaran‐Nair· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersNeuroscience

Eva L. DyerORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Transcriptomic cell type structures in vivo neuronal activity across multiple time scales.

Aidan M Schneider, Mehdi Azabou, Louis McDougall-Vigier, David F. Parks, Sahara Ensley, Kiran Bhaskaran‐Nair, Tomasz Jan Nowakowski, Eva L. Dyer, Keith B. Hengen

The paper investigates whether transcriptomic cell type can be inferred from neuronal activity timing across multiple time scales using deep learning.

Cell Reports · Mar 29, 2023Peer-reviewedNeuroscience

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers

Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer

This paper introduces a transformer architecture that models both individual and collective dynamics in time-varying systems, demonstrating its effectiveness on neural activity data.

bioRxiv · Jun 10, 2022 · PreprintNeuroscience

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity

Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer

This paper introduces Swap-VAE, an unsupervised method for learning disentangled neural activity representations using self-supervised learning on neural recordings.

bioRxiv · Jul 21, 2021 · PreprintNeuroscience