Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Rishi Rajalingham, Ha Hong, Najib Majaj, Elias Issa, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. Yamins, James J. DiCarlo
–Neural Information Processing Systems
CORnet-S, a shallow ANN with four anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a new large-scale composite of neural and behavioral benchmarks for quantifying the functional fidelity of models of the primate ventral visual stream. Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet.
Neural Information Processing Systems
Oct-3-2025, 01:07:33 GMT
- Country:
- Europe
- Belgium > Flanders
- Flemish Brabant > Leuven (0.04)
- Germany > Bavaria
- Upper Bavaria > Munich (0.04)
- Belgium > Flanders
- North America
- Canada (0.04)
- United States
- California
- San Francisco County > San Francisco (0.14)
- Santa Clara County > Stanford (0.04)
- Massachusetts > Middlesex County
- Cambridge (0.04)
- California
- Europe
- Genre:
- Research Report (0.46)
- Industry:
- Health & Medicine > Therapeutic Area > Neurology (0.71)
- Technology:
- Information Technology > Artificial Intelligence
- Cognitive Science (1.00)
- Machine Learning
- Neural Networks > Deep Learning (1.00)
- Statistical Learning (0.93)
- Representation & Reasoning (0.94)
- Vision (1.00)
- Information Technology > Artificial Intelligence