Presidential Young Investigators Award: Fast Neural Network Like Optimization Techniques

私家侦探目前对大规模研究 并行的、受干扰的和集体的计算， 人工神经系统，学习机器， 联想神经记忆，光子计算， 光学神经网络实现。 该地区 神经网络，他计划调查 各种联想神经记忆的性能 架构和相关记录/学习 算法 他推出了新的高性能 用于关联的记录/学习算法 记忆，目前正在调查的动态 这种神经记忆的各种 记录/学习算法。 计算中 和神经网络实验室，他和他的学生 正在开发无监督学习自我优化 用于聚类的神经网络架构， 无监督模式分类 在另一 项目中，神经网络架构将被用于 解决复杂的最优路径寻找问题，在两个- 和更高的维度。 他还将调查 人工神经网络的光学实现 系统. 他拥有第一个集成的专利 光学阈值门（“光学神经元”），并具有 先进的光纤交叉网络制造 技术，这是潜在的有用的光学 神经网络实现。 他最近 完成了光纤的设计与实现 光基高性能动态关联 神经记忆原型 他还感兴趣的是， 神经网络在信号中的应用 处理和并行计算。 共同努力 与韦恩州立大学神经病学的研究人员 他正在开发一种基于神经网络的 用于自动量化临床 肌电图（EMG）用于临床诊断 目的 他未来的研究将集中在适应性 无监督合成技术， 性能动态人工神经网络。 他 也将研究加快学习的方法 在多层神经网络中通过自我优化 神经结构 他未来的研究也将 包括提出和实施大规模的高- 性能光纤互连动态 联想神经记忆和其他神经记忆 网络范式。

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.