Neural cultures learn to control virtual and robotic bodies in closed-loop systems

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• Animal brains learn to control robotic bodiesfinding0.817
• Cultured neural tissue controls virtual animat behavior via learned associationsfinding0.815
  Demonstrates neural culture can learn relationships between its activity and sensory feedback without evolutionary training.
• Cultured neural networks can learn to navigate a maze via closed-loop feedback.finding0.800
  From DeMarse et al. (2001) and Bakkum et al. (2007), demonstrating learning in hybrid systems.
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.758
  Central claim about the power of connectionism.
• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.749
  Central methodological claim of the paper.
• 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 benefitsclaim0.747
  Core theoretical claim establishing that locality constraints in physical learning are not fatal—they reflect biological precedent and provide advantages like robustness and scalability
• We hypothesize that consciousness may be at the heart of a universal biological learning algorithm — one that runs on self-organizing groups of communicating cells sharing evolutionary incentives — and that it creates rather than results from organized mental architecture.hypothesis0.747
  The Genesis Hypothesis as explicit predictive conjecture
• Training corpora may produce civilizational psychologies; cross-cultural mind is embedded in model training data.claim0.747