Energy Constraints for Intelligent Behavior Modeled by Brain Metabolism

Until now, energy efficiency has not been considered a key aspect of AI, rather many AI solutions used "brute force" requiring huge amount of computing power to achieve the desired performance. Studying efficient energy consumption in brain's intelligence helps us to design AI that is energy efficient. In our approach, a system that wastes energy is not considered suitable to produce intelligence. We develop superior AI in which energy constraint leads to intelligence.

In our working model, spiking neuron populations are combined with metabolic equations in a unique way. One salient point is the coupling of processes at very different time scales: fast neuron spiking at milliseconds scale, metabolic processes at time scale of fractions of a second, and vascular effects which are several orders of magnitude slower at the scale of 10 s or longer. We compare and explore specific advantages of these approaches when studying phase transitions as hallmarks of intelligent functions.

Team members:

  • Ray Noack
  • Chetan Manjesh
  • Manuel Raimondi

GUI Illustrating the operation of the coupled Spiking NN and Metabolic Model with spike density dependent on the energy input and synchronization as the function of the metabolic feedback on the spiking model.

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