Abstract
Most, if not all, optical hardware-based neural networks are slow during the neural learning phase. This limitation has been not only a speed bottleneck, but it has contributed to the lack of wide-spread use of optical neural systems. We present a novel solution – Optical Fixed-Weight Learning Neural Networks. Standard neural networks learn new function mappings by the changing of their synaptic weights. However, the Fixed-Weight Neural Networks learn new mappings by dynamically changing recurrent neural signals. The (fixed) synaptic weights of the FWL-NN implement a learning ”algorithm” which adjusts the recurrent signals toward their proper values.
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Younger, A.S., Redd, E. (2008). Learning at the Speed of Light: A New Type of Optical Neural Network. In: Dolev, S., Haist, T., Oltean, M. (eds) Optical SuperComputing. OSC 2008. Lecture Notes in Computer Science, vol 5172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85673-3_9
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DOI: https://doi.org/10.1007/978-3-540-85673-3_9
Publisher Name: Springer, Berlin, Heidelberg
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