Orthogonal representations for robust context-dependent task performance in brains and neural networks

How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define lazyand richcoding solutions to this context-dependent decision-making problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioral testing and neuroimaging in humans and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime.

Automated summary

This study investigates how neural populations code for multiple conflicting tasks and proposes lazy and rich coding solutions. The findings suggest that the rich learning regime, which prioritizes relevant features, is consistent with neural coding patterns in biological brains.