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AHaH Computing–From Metastable Switches to Attractors to Machine Learning

Figure 1

AHaH process.

A) A first replenished pressurized container is allowed to diffuse into two non-pressurized empty containers and though a region of matter M. B) The gradient reduces faster than the gradient due to the conductance differential. C) This causes to grow more than , reducing the conductance differential and leading to anti-Hebbian learning. D) The first detectable signal (work) is available at owing to the differential that favors it. As a response to this signal, events may transpire in the environment that open up new pathways to particle dissipation. The initial conductance differential is reinforced leading to Hebbian learning.

Figure 1

doi: https://doi.org/10.1371/journal.pone.0085175.g001