Neural Decoding as Cognitive Inference

Key points

• The brain maintains stable cognition despite continuously changing neural activity.1
• Existing neural decoding methods map neural observations to predefined external labels based on the stimulus-response principle, often capturing recording-specific spurious correlations.1
• Inspired by how the brain infers the world, and specifically by Bayesian brain theory, we recast neural decoding as cognitive inference constrained by brain-intrinsic priors, yielding high-level meta-neural semantic representations.1
• In decoding experiments spanning five neural recording modalities and three cognitive domains (motor, perception and internal mentation), our cognitive inference method reorganized the geometry of neural observation representations, yielding meta-neural semantic representations that exhibited consistent geometric relationships across cognitive tasks and enabled the recovery of stable cognitive states from variable neural observations.1

Sources (1)

• [1]Neural Decoding as Cognitive Inference
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:19 PM

  How to extract stable cognitive states from variable neural observations remains a central problem in neural decoding.

  The brain maintains stable cognition despite continuously changing neural activity.

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