Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures
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Bio-inspired processes are involved more and more in today’s technologies, yet their modelling and implementation tend to be taken away from their original concept because of the limitations of the classical computation paradigm. To address this, systemic computation (SC), a model of interacting systems with natural characteristics, followed by a modelling platform with a bio-inspired system implementation were introduced. In this paper, we investigate the impact of local knowledge and asynchronous computation: significant natural properties of biological neural networks (NN) and naturally handled by SC. We present here a bio-inspired model of artificial NN, focussing on agent interactions, and show that exploiting these built-in properties, which come for free, enables neural structure flexibility without reducing performance.
KeywordsLocal Knowledge Back Propagation Systemic Computation Natural Characteristic Global Algorithm
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