Reflex Potentials

LEATR: GRAVITATIONAL NEURON SOURCE POTENTIALS

The 3 Natural Buoyancies in order for which they come, Aerospace (Zero Gravity / Vacuum), Maritime (Fluid Dynamics), Geological (Solids and Positioning) written into computer logic as a shell around other connected data and data inputs.

In Short Buoyancy to process and optimize an AI Neural Network versus weight tagging the most optimal data being transported between neurons. A Core Logic in the "Lead Edge Ash Tree Reflex", specifically the "Buoyancy Reflex Pendulum Node".

Research Artificial Intelligence Neural Network Design using Buoyancy from Reflex Potentials with your connected Data and Assets.

The Elementary Math Foundation to Natural Buoyancy is: Global Variable = (frp √ frp) to ensure the conditions of Buoyancy are met in order - foundation, reflex, performance.

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Historically Gravity Potentials in Math and Physics is to calculate the future position of two orbiting bodies like the Sun and Earth. Three bodies is said to be incalculable due to the motion of the system operating as chaos theory which is what you want so with such this means the system exist and is self-sustaining for the habitat the system is derived from. With the existing system you would only need to work with the things of it and measure those things. More formally why related equations, formulas and algorithms began around gravity regarding potentials is because you are trying to predict the location of a subject on a trajectory where the formation of potentials began around an electron stream of light split around an electrically wired vertical coil spring then brought back into a single stream on the other side. When the light is shot along the path and split around this coil with the power off nothing happens but when the power is on the spring gets hot and inside the vertical coil due to the electromagnetism a small gravity field forms contained within the vertical perimeter of the hot coil then still the light splitting around this hot coil does nothing. No particles are interacting with the coil but under certain conditions in other visible spectrums like Xray or Infrared the field or blueprint foundation of the neutrons interact but not the the particles much like you holding a small flexible tree in its current position but grabbing the roots then stretching them over to a new location while not moving the small tree. literally this is the blueprint of and specifically the neutrons interacting with the gravity field from the hot coil perhaps most commonly represented as the waveform of an atom but does not me in all situations it will be a waveform, perhaps maybe a vector of a blueprint because the subject atom is a solid, now we have something to calculate and this method is called a "Gravity Potential". The Reflex part of this extension comes from my Buoyancy Reflex Pendulum Node Shell Logic discussed in my "Lead Edge Ash Tree Reflex" my very own authored Neural Network. These shells operate on three natural layers each having to have the natural orders of buoyancy formation to be met in order Foundation, Reflex and Performance before processing the input data contained within. I Integrated the Gravity Potentials to assist and measure while having this system in mind on top of the AI Neural Network for 3D Visual Feedback and Simulation Measurements.

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