Model-agnostic neural mean field with a data-driven transfer function
As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the be...
Saved in:
Main Authors: | Alex Spaeth, David Haussler, Mircea Teodorescu |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2024-01-01
|
Series: | Neuromorphic Computing and Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1088/2634-4386/ad787f |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rugularizing generalizable neural radiance field with limited-view images
by: Wei Sun, et al.
Published: (2024-12-01) -
On a generalization of u-means
by: Francois Dubeau
Published: (1991-01-01) -
Categorization principles of modal meaning categories: a critical assessment
by: Ilse Depraetere
Published: (2015-07-01) -
Exponential turnpike property for particle systems and mean-field limit
by: Michael Herty, et al. -
Noise-agnostic quantum error mitigation with data augmented neural models
by: Manwen Liao, et al.
Published: (2025-01-01)