Ralph Linsker

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Improved local learning rule for information maximization and related applications

Ralph Linsker, R. Linsker, 2005, Neural Networks.

Synchronous neural activity in scale-free network models versus random network models.

Ralph Linsker, Geoffrey Grinstein, R. Linsker, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Computer simulation in brain science: Development of feature-analyzing cells and their columnar organization in a layered self-adaptive network

Ralph Linsker, R. Linsker, 1988 .

IBM Research Report Neural Learning of Kalman Filtering, Kalman Control, and System Identification

Ralph Linsker, 2009 .

Physical intuition and VLSI physical design

Ralph Linsker, 2008 .

Deriving Receptive Fields Using an Optimal Encoding Criterion

Ralph Linsker, R. Linsker, 1992, NIPS.

A Fully Pipelined FPGA Architecture of a Factored Restricted Boltzmann Machine Artificial Neural Network

Lok-Won Kim, Ralph Linsker, Sameh Asaad, 2014, TRETS.

A recording apparatus with a system for authentication of the recorded information

David P. DiVincenzo, Ralph Linsker, Charles H Bennet, 1997 .

Neural learning of Kalman filtering, Kalman control, and system identification

Ralph Linsker, 2009, 2009 International Joint Conference on Neural Networks.

Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network

Ralph Linsker, R. Linsker, 1992, Neural Computation.

Improving network robustness

Alina Beygelzimer, Irina Rish, Ralph Linsker, 2004, International Conference on Autonomic Computing, 2004. Proceedings..

Improving network robustness

Irina Rish, Ralph Linsker, Geoffrey Grinstein, 2004 .

Neural network learning of optimal Kalman prediction and control

Ralph Linsker, R. Linsker, 2008, Neural Networks.

Towards an Organizing Principle for a Layered Perceptual Network

Ralph Linsker, R. Linsker, 1987, NIPS.

An Iterative-Improvement Penalty-Function-Driven Wire Routing System

Ralph Linsker, R. Linsker, 1984, IBM J. Res. Dev..

Self-organization in a perceptual network

Ralph Linsker, R. Linsker, 1988, Computer.

How to Generate Ordered Maps by Maximizing the Mutual Information between Input and Output Signals

Ralph Linsker, R. Linsker, 1989, Neural Computation.

Sensory Processing and Information Theory

Ralph Linsker, R. Linsker, 1994 .

A Local Learning Rule That Enables Information Maximization for Arbitrary Input Distributions

Ralph Linsker, R. Linsker, 1997, Neural Computation.

An Application of the Principle of Maximum Information Preservation to Linear Systems

Ralph Linsker, R. Linsker, 1988, NIPS.

DEPARTMENT OF HEALTH AND HUMAN SERVICES NATIONAL INSTITUTES OF HEALTH NATIONAL LIBRARY OF MEDICINE

Ralph Linsker, Holly Buchanan, Vasiliki Karlis, 2003 .