Abstract
Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly “intelligent” systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material.
Highlights
Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system
We recognize that present technological and neuroscientific limitations may not enable researchers to replace conventional with neuromorphically driven learning systems in the near term, we propose a technique of virtual neurorobotics to rapidly forward engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intelligent, intentional interaction with humans
Running only on four processors of the cluster, the small size of the neuromorphic brain enabled NeoCortical Simulator (NCS) to interact in real time with the BRAINSTEM subsystem
Summary
Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. Despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly “intelligent” systems. For this reason, many investigators are attempting to incorporate realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has yielded quantitative parameters which characterize the brain’s interdependent electrophysiological (Markram et al, 1997; Schindler et al, 2006), genomic (Toledo-Rodriguez et al, 2004), proteomic (Toledo-Rodriguez et al, 2005), metabolomic and anatomic (Wang et al, 2006) networks. The outpouring of potentially useful data has sparked the development of over 100 neuroscience databases (Society for Neuroscience, 2007)
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