A physical learning system thus has the following ingredients: Physical d.o.f s that respond to external stimuli f by adopting a state or dynamic behavior s(f), a subset of which defines the desired behavior (i.e., output); Learning d.o.f wi that can modify how the physical d.o.f s(f; {wi}) respond to external stimuli f; A learning rule, dwi = h(s(f; {wi}))dt, that modifies the learning d.o.f wi based on the response of the physical d.o.f s to the stimuli f.

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• Reinforcement learning acting on individual characteristics affecting their connections to others can result in dynamics that are equivalent to unsupervised learning at the system scale.claim0.787
  Key insight linking individual rewards to system-level learning.
• Physical systems are more constrained in learning abilities than in silico neural networks due to locality requirements, but this mirrors biological learning constraints and offers robustness benefitsclaim0.787
  Core theoretical claim establishing that locality constraints in physical learning are not fatal—they reflect biological precedent and provide advantages like robustness and scalability
• What makes learning systems smart is that the parameters they adjust and the data to which they fit are not in the same space.claim0.773
  Distillation of why learning generalises.
• Physical learningframework0.764
  Framework for solving inverse problems in which physical systems autonomously adapt their parameters in response to stimuli through local learning rules, without requiring computational design or explicit cost functions
• To what extent can physical systems learn by exploiting typically local natural processes without any explicit cost function?question0.758
  Central research question defining the scope of physical learning; asks about achievable learning under locality constraints
• Perceptual learning is literally an integral part of value learning, necessary to integrate out dependencies on inferred causes of sensory information.claim0.755
  Core unifying claim: perception and value-learning are unified through free energy minimization.
• Cognition and learning are substrate-independent; principles of basal cognition can be implemented by any network with signals and non-linear responses to weighted inputs.claim0.742
  Core thesis that cognitive principles transcend neural substrates, enabling application to gene-regulatory, ecological, and social networks.
• Much resistance to attributing minimal consciousness to simple learning systems is driven by conflating consciousness with self-consciousnessclaim0.740
  Diagnosis of why the thesis feels counterintuitive