The P.I.'s currently interested in research in massively parallel, disturbed, and collective computations, artificial neural systems, learning machines, associative neural memories, photonic computing, and optical neural network implementations. In the area of neural networks, he plans to investigate the performance of various associative neural memory architectures and related recording/learning algorithms. He has introduced new high-performance recording/learning algorithms for associative memories, and is currently investigating the dynamics of such neural memories for various recording/learning algorithms. In the Computation and Neural Networks Laboratory, he and his students are developing unsupervised learning self-optimizing neural net architectures for clustering and unsupervised pattern classification. In another project, neural net architectures will be employed in solving complex optimal path finding problems in two- and higher-dimensions. He will also be investigating the optical implementations of artificial neural systems. He holds a patent for the first integrated optical threshold gate ("optical neuron") and has developed fiber optic cross-bar network fabrication techniques, which are potentially useful in optical neural network implementations. He has recently completed the design and implementation of a fiber optic-base high-performance dynamic associative neural memory prototype. He is also interested in the application of neural networks in signal processing and parallel computing. In a joint effort with researchers at Wayne State's Neurology Department, he is developing a neural-network-based technique for automatic quantification of the clinical Electromyogram (EMG) for clinical diagnostic purposes. His future research will focus on adaptive unsupervised synthesis techniques for high- performance dynamic artificial neural networks. He will also be investigating ways of speeding up learning in multiple-layer neural nets through self-optimizing neural architectures. His future research will also involve proposing and implementing large scale high- performance fiber optic interconnected dynamic associative neural memories and other neural network paradigms.
