A single computational objective drives specialization of streams in visual cortex
Finzi, D.; Margalit, E.; Kay, K.; Yamins, D. L. K.; Grill-Spector, K.
Show abstract
Human visual cortex is organized into dorsal, lateral, and ventral streams. A long-standing hypothesis is that the functional organization into streams emerged to support distinct visual behaviors. Here, we use a neural network-based computational model and a massive fMRI dataset to investigate why visual streams emerge. We find that models trained for stream-specific visual behaviors poorly capture brain responses and organization. Instead, a self-supervised Topographic Deep Artificial Neural Network, which encourages nearby units to respond similarly, successfully predicts brain responses, spatial segregation, and functional differentiation across streams. These findings challenge the prevailing view that streams evolved to separately support different behaviors and suggest instead that functional organization can arise from a single principle: learning generally useful visual representations subject to local spatial constraints.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A confirmation bias in perceptual decision-making due to hierarchical approximate inference 96%
- Redundant representations are required to disambiguate simultaneously presented complex stimuli 96%
- Dynamic Predictive Coding: A Model of Hierarchical Sequence Learning and Prediction in the Neocortex 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.