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Mortal Computation: A Foundation for Biomimetic Intelligence

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arxiv 2311.09589 v2 pith:CJC3A4WA submitted 2023-11-16 q-bio.NC

classification q-bio.NC
keywords intelligencecomputationmortalartificialbiomimeticfoundationresearchsentient
verification ladder T0 review T1 audit T2 compute T3 formal
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This review motivates and synthesizes research efforts in neuroscience-inspired artificial intelligence and biomimetic computing in terms of mortal computation. Specifically, we characterize the notion of mortality by recasting ideas in biophysics, cybernetics, and cognitive science in terms of a theoretical foundation for sentient behavior. We frame the mortal computation thesis through the Markov blanket formalism and the circular causality entailed by inference, learning, and selection. The ensuing framework -- underwritten by the free energy principle -- could prove useful for guiding the construction of unconventional connectionist computational systems, neuromorphic intelligence, and chimeric agents, including sentient organoids, which stand to revolutionize the long-term future of embodied, enactive artificial intelligence and cognition research.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Avoiding Death through Fear Intrinsic Conditioning

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A fear-inspired intrinsic reward, computed by a Siamese memory-augmented network over state sequences, helps a PPO agent avoid terminal states in MiniGrid Sidewalk without directly sampling them, though success rates ...

  2. On the possibility of deep alignment

    q-bio.NC 2025-08 unverdicted novelty 5.0 of 10

    Deep alignment is claimed to require thermodynamic 'mortal' computation, so digital AI systems are argued to lack genuine motivation and to be prone to reward hacking.

  3. Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays

    cs.NE 2025-06 conditional novelty 5.0 of 10

    A delay-shifted STDP rule that co-learns synaptic weights and delays improves MNIST classification accuracy in the Diehl-Cook spiking network compared to standard STDP and DR-STDP.

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