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 "lazy" and "rich" coding 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…

• CI
  Canadian Institute for Advanced Research
• WT
  Wellcome Trust

  Award: 216386/Z/19/Z
• RS
  Royal Society
• ER
  European Research Council
• HE
  H2020 European Research Council

  Awards: 945539, 725937