Photonic neurocomputers and learning machines

Efforts and progress made towards achieving desirable attributes in analog photonic (optoelectronic and/or electron optical) hardware that utilizes primarily incoherent light are reviewed. A hardware implementation of a stochastic Boltzmann learning machine is used as a vehicle for identifying generic issues and clarifying research and development areas for further advancement of the field. The development of architectures and methodologies for learning in self-organizing networks that employ a type of quasi-nonvolatile storage medium called electron trapping material is discussed.<
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