Le Martelot, E; Bentley, PJ; Lotto, RB; (2009) Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures. In: Suzuki, Y and Hagiya, M and Umeo, H and Adamatzky, A, (eds.) NATURAL COMPUTING, PROCEEDINGS. (pp. 122 - 133). SPRINGER
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Bio-inspired processes are involved more and more in today's technologies, vet 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 awl 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.
|Title:||Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures|
|Event:||2nd International Workshop on Natural Computing|
|Dates:||2007-12-10 - 2007-12-13|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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