Telescoping Network Reinstatement in Self-Aware Networks

Abstract

This paper develops a Self-Aware Networks (SAN) hypothesis about how a learned state in one dendritic arbor can help regenerate a much larger three-dimensional perceptual or mnemonic configuration. The proposal combines nonlinear dendritic pattern recognition, oscillatory winner-take-most competition, recurrent excitation and inhibition, and Neural Array Projection Oscillation Tomography (NAPOT). During learning, a distributed event changes synapses, dendritic compartments, cellular excitability, and circuit relationships. In the stronger SAN interpretation, a dendritic arrangement can store a three-dimensional relational projection of the network neighborhood that surrounded the neuron during the event. During retrieval, a partial cue activates that learned disposition. Its conclusions are limited to the assumptions, evidence, and testing conditions stated in the manuscript. It belongs to the Self-Aware Networks neuroscience and consciousness research program. This is a corrected preprint edition released under the Creative Commons Attribution 4.0 license.