Author

Sun Minni

Mortimer B. Zuckerman Mind Brain Behavior Institute

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

Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Organism:Drosophila· 1 Organism:human· 1 Approach:statistical model· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Andrew F. Siliciano· 1 shared Chad Morton· 1 shared Charles K. DowelliD· 1 shared Grace W. LindsayiD· 1 shared Guangyu Robert YangiD· 1 shared Hossein AdeliiD· 1 shared Juliana Y. RheeiD· 1 shared Kenneth D. MilleriD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 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

1 shared paperNeuroscience

Andrew F. Siliciano

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A vector-based strategy for olfactory navigation in Drosophila

Andrew F. Siliciano, Sun Minni, Chad Morton, Charles K. Dowell, Noelle B. Eghbali, Silas E. Busch, Juliana Y. Rhee, Larry F. Abbott, Vanessa Ruta

This study shows that Drosophila use vector-based memory computations in the central complex to navigate odor plumes by tracking their boundaries.

Nature · Jul 22, 2026Peer-reviewedNeuroscience

Predicting brain activity using Transformers

Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte

This paper uses a transformer model to predict human brain activity from images, achieving a challenge score of 63.5229 and demonstrating the effectiveness of self-supervised transformer features.

bioRxiv · Aug 2, 2023 · PreprintNeuroscience

Understanding the functional and structural differences across excitatory and inhibitory neurons

Sun Minni, Li Ji-An, Theodore Moskovitz, Grace W. Lindsay, Kenneth D. Miller, Mario Dipoppa, Guangyu Robert Yang

The paper uses an artificial neural network to study why excitatory and inhibitory neurons differ in selectivity and connectivity, finding that these differences can be explained by a few factors.

Conference on Cognitive Computational Neuroscience (CCN) · Jan 1, 2019Peer-reviewedNeuroscience