Applications
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  • Hugely parallel, distributed, massive-scale computer systems. Systemic computation is a different way of doing multi-core or cluster. Forget centralisation, lose the synchronisation, and take advantage of the power of biological computation.
  • Self-adaptive, embodied and continuously running devices, able to modify themselves and adapt to different environments. Systemic computation enables software to keep running despite changes to the hardware and software around it, e.g. in large software ecosystems, or even in planetary explorer robots which may encounter unforeseen environments and problems.
  • Robust, autonomous and self-repairing computing devices, able to withstand harsh environments. For example, the controller in a satellite or unmanned autonomous vehicle, or a dynamic sensor network able to withstand damage and loss of resources while maintaining functionality.
  • Self-adaptive, embodied and continuously running devices, able to modify themselves and adapt to different environments. For example, software which needs to keep running despite changes to the hardware and software around it, or planetary explorer robots which may encounter unforeseen environments and problems.
  • Massively parallel, stochastic, and complex models of natural systems such as biological systems or physically-interacting systems. For example, models of neurogenesis, immunobiology, or the formation of black holes.
  • Fast and efficient implementations of bio-inspired algorithms such as genetic algorithms, developmental algorithms, swarm intelligence, artificial immune systems, neural networks.
  • Computational analyses of natural systems. For example, analyses of information flow, structural change, transitions in complexity, or evolvability of a system.
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