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Frontiers in Neuromorphic Engineering

Frontiers in Neuroscience
Frontiers Media SA
Publication Date
DOI: 10.3389/fnins.2011.00118
  • Neuroscience
  • Specialty Grand Challenge
  • Biology
  • Communication
  • Computer Science
  • Design
  • Engineering
  • Mathematics


Frontiers in neuromorphic engineering Frontiers in neuromorphic engineering Giacomo Indiveri1* and Timothy K. Horiuchi2 1 Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland 2 Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland, College Park, MD, USA *Correspondence: [email protected] Neurobiological processing systems are remarkable computational devices. They use slow, stochastic, and inhomogeneous computing elements and yet they outper- form today’s most powerful computers at tasks such as vision, audition, and motor control, tasks that we perform nearly every moment that we are awake without much conscious thought or concern. Despite the vast amount of resources dedicated to the research and development of computing, information, and communication technol- ogies, today’s fastest and largest computers are still not able to match biological sys- tems at robustly accomplishing real-world tasks. While the specific algorithms and representations that biological brains use are still largely unknown, it is clear that instead of Boolean logic, precise digital representations, and synchronous opera- tions, nervous systems use hybrid analog/ digital components, distributed represen- tations, massively parallel mechanisms, combine communications with memory and computation, and make extensive use of adaptation, self-organization, and learn- ing. On the other hand, as with many suc- cessful man-made systems, it is clear that biological brains have been co-designed with the body to operate under a specific range of conditions and assumptions about the world. Understanding the computational prin- ciples used by the brain and how they are physically embodied is crucial for develop- ing novel computing paradigms and guid- ing a new generation of technologies that can combine the strengths of industrial- scale electronics with the computational performance of brains. Neur

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