Orbit-Spin Coupling, the Solar Dynamo, and the Planetary Theory of Sunspots

Orbit spin coupling is proposed as an alternative to planetary tidal models for the excitation of solar variability as a function of time. Momentum sourced from the orbital angular momenta of solar system bodies is deposited within the circulating fluid envelopes of the Sun and planets in this hypothesis. A reversing torque acts about an axis lying within the Sun's equatorial plane. The torque gives rise to tangential differential accelerations of solar materials as a function of longitude, latitude, depth, and time. The accelerations pulse in amplitude, and change sign, on timescales corresponding to the periods, beats, and harmonics of inner and outer planet orbital motions. In contrast to planetary tidal models, no special amplification mechanism may be required, as estimated peak accelerations are about 2 orders of magnitude larger than the largest tidal accelerations. Organized mass motions driven by the torque may be incorporated in dynamo simulations through the flow velocity term of the MHD induction equation. The spatiotemporal variability of flow velocities may then influence the variability with time of solar magnetic activity. We provide torque values at 1 day timesteps for the years 1660 to 2220. We discuss the time variability of the torque in juxtaposition with SIDC monthly sunspot numbers from 1750 to present. We investigate Hale cycle synchronization, and the variability with time of the total solar irradiance, with reference to outer and inner planet contributions respectively. We propose a 3 component model for understanding and simulating the solar magnetic cycle, which includes processes internal to the Sun, external forcing, due to orbit spin coupling, and a time-delay, or system memory, component. This model supplies a physical explanation for the observed variability with time of Schwabe cycle periods and Hale cycle periods from 1712 to present.Comment: 95 pages, 8 Figure

Generalisation of structural knowledge in the hippocampal-entorhinal system

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments