Framework and implications of virtual neurorobotics

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ID: 50788
2008
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Abstract
Despite decades of societal investment in artificial learning systems, truly “intelligent” systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain “algorithm” itself—trying to replicate uniquely “neuromorphic” dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain’s interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or “avatars”, to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications.
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nolastname2008frameworkfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
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Journal Frontiers in neuroscience
Year 2008
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http://journal.frontiersin.org/Journal/10.3389/neuro.01.007.2008/full
Keywords
Zoology Biology (General) Botany Medicine Science Agriculture (General) environmental effects of industries and plants renewable energy sources environmental sciences neurosciences. biological psychiatry. neuropsychiatry agriculture public aspects of medicine social sciences (general) arctic medicine. tropical medicine environmental law economic growth, development, planning

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