Privileged representational axes in biological and artificial neural networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 20, 2024

Abstract How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings (or ‘axes’) neural responses. But might these representational axes matter, with some privileged systematically over others? To find out, we developed methods test for alignment tuning across brains deep convolutional networks (DCNNs). Across both vision audition, DCNNs consistently favored certain representing natural world. Moreover, trained on inputs were aligned those in perceptual cortices, axis-sensitive model-brain similarity metrics better differentiated competing models biological sensory systems. We further show coding schemes privilege can reduce downstream wiring costs improve generalization. These results motivate a new framework understanding artificial its computational benefits.

Language: Английский

Conclusions about Neural Network to Brain Alignment are Profoundly Impacted by the Similarity Measure

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 9, 2024

Abstract Deep neural networks are popular models of brain activity, and many studies ask which provide the best fit. To make such comparisons, papers use similarity measures as Linear Predictivity or Representational Similarity Analysis (RSA). It is often assumed that these yield comparable results, making their choice inconsequential, but it? Here we if how measure affects conclusions. We find influences layer-area correspondence well ranking models. explore choices impact prior conclusions about most “brain-like”. Our results suggest widely held regarding relative alignment different network with activity have fragile foundations.

Language: Английский

Brain Decodes Deep Nets

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Journal Year: 2024, Volume and Issue: 364, P. 23030 - 23040

Published: June 16, 2024