None of these methods are direct guides to the minimum possible FLOP/s budget, as the most efficient ways of performing tasks need not resemble the brainРІР‚в„ўs ways, or those of current artificial systems. Use the communication bandwidth in the brain as evidence about its computational capacity (the РІР‚Сљ communication methodРІР‚Сњ).Use the brainРІР‚в„ўs energy budget, together with physical limits set by LandauerРІР‚в„ўs principle, to upper-bound required FLOP/s (the РІР‚Сљ limit methodРІР‚Сњ).Identify a portion of the brain whose function we can already approximate with artificial systems, and then scale up to a FLOP/s estimate for the whole brain (the РІР‚Сљ functional methodРІР‚Сњ).Estimate the FLOP/s required to model the brainРІР‚в„ўs mechanisms at a level of detail adequate to replicate task-performance (the РІР‚Сљ mechanistic methodРІР‚Сњ).I consulted with more than 30 experts, and considered four methods of generating estimates, focusing on floating point operations per second (FLOP/s) as a metric of computational power. How powerful is sufficiently powerful? I investigated what we can learn from the brain about this. LetРІР‚в„ўs grant that in principle, sufficiently powerful computers can perform any cognitive task that the human brain can.

Overall FLOP/s for other signaling mechanisms.Crabs, locusts, and other considerations.

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