Sketch of a novel approach to a neural model
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In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical integration model of neural processing. We believe a new approach to neural modeling will benefit the 'third wave' of AI. The 'horizontal' plane consists of an adaptive network of neurons connected by transmission links which generates spatio-temporal spike patterns. This fits with standard computational neuroscience approaches. Additionally for each individual neuron, there is a 'vertical' part consisting of internal adaptive parameters steering the external membrane-expressed parameters which are involved in neural transmission. Each neuron has a vertical modular system of parameters, corresponding to (a) external parameters at the membrane layer, divided into compartments (spines, boutons), (b) internal parameters in the submembrane zone and the cytoplasm with its intracellular protein signaling network and (c) 'core' parameters in the nucleus for genetic and epigenetic information. In such models, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are systematically separated, an important conceptual advance over synaptic weight models. We discuss the membrane-based ('external') selection of outside signals for processing vs signal loss by fast fluctuations and the neuron-internal computing strategies from intracellular protein signaling to the nucleus as the 'core' system. We want to show that the individual neuron has an important role in the processing of signals and that many assumptions derived from the synaptic weight adjustment hypothesis of memory may not hold in a real brain. Not every transmission event leaves a trace and the neuron is a self-programming device rather than passively determined by ongoing input. Ultimately we strive to build a flexible memory system that processes facts and events automatically.
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