Neuromorphic electronic systems
1990/01/01 by C. Mead, Carver Mead · 2,295 citations
Computer Science · Engineering · Materials Science · #Adaptation (eye) #Advanced Memory and Neural Computing #Algorithm #Analog computer #Artificial intelligence #Artificial neural network #Component (thermodynamics) #Computation #Computer science #Electrical engineering #Engineering #Neural Networks and Applications #Neuromorphic engineering #Representation (politics) #Scale (ratio) #Transition Metal Oxide Nanomaterials
paper · doi:10.1109/5.58356
published in Proceedings of the IEEE 78(10), 1629-1636 (Institute of Electrical and Electronics Engineers)
openalex publication_date 1990/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
It is shown that for many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer-scale silicon fabrication.>
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