Elastic Nanocomputation in an Ideal Brain (1p abstract + 36 pages + 49 endnotes)

This explanation of what a brain is and does rests on informational first principles, because information theory, like its parent theory thermodynamics, is mathematically sacrosanct, itself resting on real-valued probability. Just as thermodynamics has enabled hyper-potent physical technologies from the internal combustion engine to the hydrogen bomb, so information theory has enabled hyper-persuasive technologies, from color television to addictive video games. Only a theory of what a brain is and does based on those same principles makes legible and transparent the mechanisms by which such hyper-persuasion works. In information-theoretic terms, a brain is a specialized real-valued real-time 3-D processor detecting discontinuities in spacetime outside itself and reconstituting in itself a continuous reality based on them. This continuous approach is difficult to reconcile with any computational architecture based on separate neurons, and in fact the vast discrepancy in efficiency (of order at least 10 8) between those efficiencies constitutes this paper's calculations. This remarkable signal-processing requires strong prior hypotheses embedded in 3-D edge-detecting algorithms, priors which unfortunately also open an unpatchable security hole to automated persuasion. So a 3-D model of the brain is essential for understanding how and why persuasive technologies alter our perception of reality, and for protecting us against systemic, systematic cognitive manipulation.