Quantum tunneling, deep learning, and the engram

It does not exist a single specific protein/gene encoding for a single specific information-memory bit although the cell machinery of memory long-term potentiation, was extensively studied. Indeed, the brain engram, remains a knowledge gap. The theory of exclusion led us to cancel one-by-one several unrealistic biological options, suggesting that the explanation resides somewhere else. Superposition of up to concentric 300 myelin layers, spiraled, and highly compacted wrapping a single axon and each wrap could host hundreds to thousands niches, as “memory cells”, collectively consisting of a massive array of cells. The disjointed 3D spatial superposition; allows storage of charges, nodes not facing from a layer to next. The thickness of a single myelin layer is ranging from 7.0 to 20 nm. The dimension scale is approximately the exact dimensions of the charge trap, the tunnel and dielectric also equipping current AI microchips. Stored charges are positive ions, with similar effect whether charges are negative or positive charges creating an electromagnetic field. To "write" data, following an action potential, this voltage applies to the control gates of the myelin layers producing an ionic charge injection. This causes charges to gain energy and "tunnel" through the myelin layer across Ranvier nodes, via quantum tunneling, and deep into the concentric myelin multilayers. This is creating an insulated trapping of K+ ions isolated from the system. The injected ionic charges (K+) become physically stuck in "traps" within the myelin layers. The K+ ions may not move freely, completely trapped after AP ceases. Mirroring a single-bit, single-level-cell, a trapped ionic charge (ions K+) may represent a "1," while an empty cell (absence of K+) represents a "0". Quantum physics cannot be measured (Heisenberg Uncertainty Principle). The trial-and-error process, with a Bayesian inference which may have also been the core evolution of the learning human brain. Based on selected mathematical equations, we analyzed the general scheme on how deep learning may be embedded in the brain.