An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Neuromorphic architectures for nanoelectronic circuits: Research Articles
International Journal of Circuit Theory and Applications - Nanoelectric Circuits
Hybrid CMOS/nanoelectronic digital circuits: devices, architectures, and design automation
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Defect-tolerant nanoelectronic pattern classifiers: Research Articles
International Journal of Circuit Theory and Applications - Nanoelectronic Circuits
CMOL crossnets as pattern classifiers
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Functional abilities of a stochastic logic neural network
IEEE Transactions on Neural Networks
Global Reinforcement Learning in Neural Networks
IEEE Transactions on Neural Networks
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CMOL CrossNets, hybrid CMOS/nanoelectronic neuromorphic circuits, may open up exciting opportunities to build artificial intelligence similar to the brain. However, limited functionality of nanodevices used in CMOL circuits causes significant challenges to train CrossNets with the usual algorithms. In order to overcome these challenges, we developed an in-situ variety of the error backpropagation method for supervised training of CrossNet-based pattern classifiers. Although this algorithm successfully trained CrossNets to perform simple benchmark classification tasks in Proben1, we found that it did not scale up to larger problems such as the MNIST dataset. Therefore, we propose an alternative in-situ method, combining training with the hidden layer build-up. Simulated results suggest that our new in-situ approach is appropriate to train CrossNets to perform classification on practical problems.