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Artificial neural networks seek to mimic – in silico – what the biological brain does naturally: real-time parallel processing of massive data sets. Now, the Engineering Faculty’s Prof. Zeev Zalevsky, together with post doctoral researcher Dr. Eyal Cohen and Zalevsky’s colleague from Hebrew University Dr. Mickey London, has presented the first-ever conceptual design for an in-fiber optical neural network – a portable, photonic processor in which light-based signals are shared within a “feed forward” neural network computational structure.  Not only does this patent-pending system demonstrate high-speed parallel processing with low power consumption, it also achieves something that we take for granted when it happens between our ears: by taking in and processing external data, this optical neural network can learn.