Abstract
Many physiology experiments demonstrate that an organism’s cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perceptual information and the existing neural network is introduced to absorb the novel sensory pathway. The experimental results show that the computational model can effectively integrate sudden emerged sensory channels and the neural circuits in the computational model can be reused for novel modalities without influencing the original modality.
Highlights
Many physiological experiments have demonstrated the expandability of an organism’s cortex-receptor system
The model is different from the classic artificial neuron model whose synapse structure is fixed[8], which implies a non-extendibility for the novel sensory signals
A hierarchical and modularized computational structure is proposed to enable novel information flow be integrated in different concept levels
Summary
Many physiological experiments have demonstrated the expandability of an organism’s cortex-receptor system. The rat and monkey experiments imply that the sensing capability of an organism can be expanded artificially throughout the organism’s lifetime, differing from the natural way, where organisms acquire new experiences through genetic variation and those sensory transducers are fixed at birth[4,5]. To provide a mathematical theory for the cortex-receptor artificial extension, in this study, I build a computational model that can adapt to a novel sensory pathway in an online manner. Inspired by the phenomenon that novel experience can induce a formation of new spines in the brain[6,7], a synapse expandable artificial neuron model that can grow new synapses to absorb extended sensory signal during learning is designed. The model is different from the classic artificial neuron model whose synapse structure is fixed[8], which implies a non-extendibility for the novel sensory signals. The synapse expandable artificial neuron can work on any level of the computational structure, which provides an adequate theoretical explanation to the cortex-receptor artificial extension
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