Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes
Summary: We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish be...
Saved in:
| Main Authors: | Daniela Egas Santander, Christoph Pokorny, András Ecker, Jānis Lazovskis, Matteo Santoro, Jason P. Smith, Kathryn Hess, Ran Levi, Michael W. Reimann |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224028128 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods
by: Shakti P. Padhy, et al.
Published: (2025-01-01) -
Semantic and relation aware neural network model for bi-class multi-relational heterogeneous graphs
by: Yufei Zhao, et al.
Published: (2025-04-01) -
Cooperative evolution with reward-based heterogeneous tax in spatial public goods games
by: Li Yue, et al.
Published: (2025-02-01) -
Robust generalization of tuning to self-induced sensation
by: Rozana Ovsepian, et al.
Published: (2025-06-01) -
Auditory evoked delta brushes involve stimulus-specific cortical networks in preterm infants
by: Anna Kaminska, et al.
Published: (2025-05-01)