Neuromorphic
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The term neuromorphic was coined by Carver Mead, in the late 1980s to describe Very Large Scale Integration (VLSI) systems containing electronic analog circuits that mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe both analog, digital or mixed-mode analog/digital VLSI systems that implement models of neural systems (for perception, motor control, or sensory processing) as well as software algorithms.
A key aspect of neuromorphic design is understanding how the morphology of individual neurons, circuits, and overall architectures create desirable computations, affect how information is represented, influences robustness to damage, incorporates learning and development, and facilitates evolutionary change.
Neuromorphic Engineering is a new interdisciplinary discipline that takes inspiration from biology, physics, mathematics, computer science and engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.
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- Holographic Neural Technology (HNeT) based on non linear phase coherence/decoherence principles. The analogy has been made to quantum processes through the core synaptic algorithm which has strong similarities to the QM wave equation.
- The yearly Neuromorphic Engineering Workshop brings together young investigators and more established researchers from academia with their counterparts in industry and national laboratories, working on both neurobiological as well as engineering aspects of sensory systems and sensory-motor integration.
- The Institute of Neuromorphic Engineering is an international institute "without walls" dedicated to the advancement of research and education in neuromorphic engineering.
- The official INE news site.
Below is a list of leading researchers in various areas of neuromorphic engineering, based on an article IEEE Spectrum (May, 2006):
Vision and Robotics
- Geoffrey Barrows (Centeye Inc.)
- Kwabena Boahen (Stanford University)
- Tobi Delbruck, Shih-Chii Liu, and Giacomo Indiveri (ETH Zurich)
- Ralph Etienne-Cummings (Johns Hopkins University)
- Nicolas Franceschini (CNRS, France)
- Reid Harrison (University of Utah)
- Charles Higgins (University of Arizona)
- Timothy Horiuchi (University of Maryland)
- Rahul Sarpeshkar (Massachusetts Institute of Technology)
- Bertram Shi (Hong Kong University of Science and Technology)
- Steve de Weerth (Georgia Institute of Technology)
Hearing, Sonar, and Speech Processing
- Andreas Andreou (Johns Hopkins)
- Gert Cauwenberghs (University of California, San Diego)
- Timothy Horiuchi and Shihab Shamma (University of Maryland)
- Rahul Sarpeshkar (Massachusetts Institute of Technology)
- Andre van Schaik (University of Sydney)
Neural Processing and Processing and Learning
- Gert Cauwenberghs (University of California, San Diego)
- Chris Diorio (University of Washington)
- Paul Hasler (Georgia Tech)
- Giacomo Indiveri (ETH Zurich)
- Rahul Sarpeshkar (Massachusetts Institute of Technology)
- FACETS: Fast Analog Computing with Emergent Transient States (European Commission Research Project)
Spike-Based Processing
- Kwabena Boahen (Stanford University)
- Giacomo Indiveri (ETH Zurich)
- Alan Murray and Alister Hamilton (University of Edinburgh)
- Tor Sverre Lande (University of Oslo)
- Shih-Chii Liu (ETH Zurich)
- Rahul Sarpeshkar (Massachusetts Institute of Technology)
- FACETS: Fast Analog Computing with Emergent Transient States (European Commission Research Project)
Biomedical and Ultra-Low-Power Applications
- Reid Harrison (University of Utah)
- Rahul Sarpeshkar (Massachusetts Institute of Technology)
- Christofer Toumazou (Imperial College)
(See Leading Labs)
Other Links
- The Center for Neuromorphic Systems Engineering at the California Institute of Technology.
- The Computation and Neural Systems department at the California Institute of Technology.