PreprintAll rights reservedbioRxiv
Neural networks as decision trees: an analytical framework for learning and neural selectivity
- 1Ecole Normale Superieure
Related
- Neural networks as decision trees: an analytical solution for learning and neural selectivity
- A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks
- Selectivity Dilution in Three-Factor Learning: A Finite-Population Model with Potential Implications for Motor Development and Post-Stroke Motor Rehabilitation
- Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
- Exploring the role of multivariate associations between brain networks and gut microbiota in (food) value-based decision-making
BibTeX
@misc{tissot2026neural,
title = {{Neural networks as decision trees: an analytical framework for learning and neural selectivity}},
author = {H. Tissot and J. Ranft and Y. Boubenec},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.23.753718},
url = {https://www.biorxiv.org/content/10.64898/2026.09.23.753718v1},
}
RIS
TY - UNPB
TI - Neural networks as decision trees: an analytical framework for learning and neural selectivity
AU - H. Tissot
AU - J. Ranft
AU - Y. Boubenec
PY - 2026
DA - 2026/09/23
PB - bioRxiv
DO - 10.64898/2026.09.23.753718
UR - https://www.biorxiv.org/content/10.64898/2026.09.23.753718v1
ER -