We present a new image classification model that extends CNNs with biologically-inspired higher-order convolutions. Outperforms standard CNNs on benchmarks and shows unique representational properties, bridging neuroscience and deep learning.

Emergence of an alignment between LLMs' and the brain's computational dynamics, and key factors allowing it : scaling and context size.