An Iterative-Improvement Penalty-Function-Driven Wire Routing System. - Ralph Linsker (IBM J. Res. Dev., 1984)

Towards an Organizing Principle for a Layered Perceptual Network. - Ralph Linsker (NIPS, 1987)

An Application of the Principle of Maximum Information Preservation to Linear Systems. - Ralph Linsker (NIPS, 1988)

Self-Organization in a Perceptual Network. - Ralph Linsker (Computer, 1988)

How to Generate Ordered Maps by Maximizing the Mutual Information between Input and Output Signals. - Ralph Linsker (Neural Comput., 1989)

Deriving Receptive Fields Using an Optimal Encoding Criterion. - Ralph Linsker (NIPS, 1992)

Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network. - Ralph Linsker (Neural Comput., 1992)

A Local Learning Rule That Enables Information Maximization for Arbitrary Input Distributions. - Ralph Linsker (Neural Comput., 1997)

Improving Network Robustness. - Alina Beygelzimer; Geoffrey Grinstein; Ralph Linsker; Irina Rish (ICAC, 2004)

Improved local learning rule for information maximization and related applications. - Ralph Linsker (Neural Networks, 2005)

Neural network learning of optimal Kalman prediction and control. - Ralph Linsker (Neural Networks, 2008)

Neural learning of Kalman filtering, Kalman control, and system identification. - Ralph Linsker (IJCNN, 2009)

A Fully Pipelined FPGA Architecture of a Factored Restricted Boltzmann Machine Artificial Neural Network. - Lok-Won Kim; Sameh W. Asaad; Ralph Linsker (ACM Trans. Reconfigurable Technol. Syst., 2014)