Author

Samuel Schmidgall

George Mason UniversityORCID 0000-0001-8192-9337Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Organism:C. elegans· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Catherine D. SchumaniD· 1 shared Joe HaysiD· 1 shared Maryam ParsaiD· 1 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 1 mutual co-author link

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1 shared paperNeuroscience

Catherine D. SchumanORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintSep 30, 2022

Biological connectomes as a representation for the architecture of artificial neural networks

Samuel Schmidgall, Catherine D. Schuman, Maryam Parsa

The paper explores how biological connectomes can inform artificial neural network design, showing that architectural statistics from biological circuits provide a useful prior for machine learning tasks.

1 citation

C. elegansstatistical model

𝕏 PostBluesky

NeurosciencebioRxivPreprintJun 24, 2022

Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

Samuel Schmidgall, Joe Hays

The paper proposes training spiking neural networks with neuromodulated synaptic plasticity using gradient descent to improve online learning performance inspired by neuroscience.

3 citations

machine learning (classical)

𝕏 PostBluesky