LEARNING TO LEARN, SELF-IMPROVEMENT, METALEARNING, META-LEARNING - Self-referential learning machines - self-reference. 14. J. Schmidhuber. A general method for incremental self-improvement and multiagent learning. In X. Yao, editor, Evolutionary Computation: Theory and Applications. Chapter 3, pp.81-123, Scientific Publ. 13. 12. 11. 10. 9. 8. 7. 6. 5. 4. 3. 2. 1. In 1992 Schmidhuber suggested that recurrent neural networks (RNNs) can be used to metalearn learning algorithms.
Cadiesingularity. Regenerating nerve cells: Research offers hope in new treatment for spinal cord injuries. Rutgers researchers have developed an innovative new treatment that could help minimize nerve damage in spinal cord injuries, promote tissue healing and minimize pain. After a spinal cord injury there is an increased production of a protein (RhoA) that blocks regeneration of nerve cells that carry signals along the spinal cord and prevents the injured tissue from healing. Scientists at the W.M. Keck Center for Collaborative Neuroscience and Quark Pharmaceuticals Inc. have developed a chemically synthesized siRNA molecule that decreases the production of the RhoA protein when administered to the spine and allows regeneration of the nerve cells.
"It is exciting because this minimally-invasive treatment can selectively target the injured tissue and thereby promote healing and reduce pain," says Martin Grumet, associate director of the Keck Center and senior author of a recent study published in the Journal of Neurotrauma. Anthony Atala: Printing a human kidney. Neural Networks. Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. Contents: 1. 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison 2. 2.1 How the Human Brain Learns? 2.2 From Human Neurones to Artificial Neurones 3. 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron 4. 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons 5. 5.1 Transfer Function 5.3 The Back-Propagation Algorithm.
Researchers Create the First Artificial Neural Network Out of DNA. PASADENA, Calif. —Artificial intelligence has been the inspiration for countless books and movies, as well as the aspiration of countless scientists and engineers. Researchers at the California Institute of Technology (Caltech) have now taken a major step toward creating artificial intelligence—not in a robot or a silicon chip, but in a test tube. The researchers are the first to have made an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete patterns, just as a brain can. "The brain is incredible," says Lulu Qian, a Caltech senior postdoctoral scholar in bioengineering and lead author on the paper describing this work, published in the July 21 issue of the journal Nature. "It allows us to recognize patterns of events, form memories, make decisions, and take actions.
So we asked, instead of having a physically connected network of neural cells, can a soup of interacting molecules exhibit brainlike behavior? " Regenerating brain cells.