“Reassessing Hierarchical Correspondences between Brain and Deep Networks through Direct Interface”, Nicholas J. Sexton, Bradley C. Love2022-07-13 (CNN, neuroscience)⁠:

Functional correspondences between deep convolutional neural networks (DCNNs) and the mammalian visual system support a hierarchical account in which successive stages of processing contain ever higher-level information. However, these correspondences between brain and model activity involve shared, not task-relevant, variance.

We propose a stricter account of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully drive the DCNN’s object recognition decision.

Using this approach on 3 datasets, we found that all regions along the ventral visual stream best corresponded with later VGG model layers, indicating that all stages of processing contained higher-level information about object category.

Time course analyses suggest that long-range recurrent connections transmit object class information from late to early visual areas.

See Also:

• The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

• The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing

• Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

• Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

• Neural population control via deep image synthesis

• Neural System Identification with Neural Information Flow

• Deep learning models of cognitive processes constrained by human brain connectomes

• Deep image reconstruction from human brain activity