NeuralComputers.us
Neural Network Computing

NeuralComputers.us is currently an informational website on the general principles and use of neural network computing in various sectors. Future development may include in-depth coverage of one or more specific applications. Both organic and inorganic neural networks process information using highly parallel, distributed, probabilistic signal processing. Neural networks excel at order recognition problems that are not well addressed by sequential processing -- incorporating voice recognition, positioning, weather prediction, economic forecasting, and organic thinking. Although virtual neural networks will likely learn, they regularly do not learn completely on their own. Generally, their knowledge is guided by: initial design of the network form, processing algorithms, and variable settings by a human supervisor; choice of the number and types of cases on which they are trained; and practical usage settings. Giving direction to neural network education can be as much an "art" as it is a science. With respect to just the one question of how a large number of cases to use in instruction -- networks must train on hundreds, or even thousands, of cases in order to make useful predictions for most types of problems. Evolutionary biology is the study of the origin and modification of species over time. Core principles include genetic drift and natural selection. Modern evolutionary biology makes use of techniques from the subject of nanoscopic biology to map and compare DNA sequences. In the realm of biology, chains of ganglia evolved into centralized brains with complex neural networks. In the locations of computer science and virtual reasoning, hard-wired sequential processes are emerging into parallel, adaptive networks. Whether animate and inanimate evolution will converge remains unknown, but yields ample plots for science fiction. One of the signature attributes of living life is the capability to learn and adapt to environmental modifies. This is furthermore the goal of simulated neural networks. In the human brain, learning and adaptation is thought to transpire in changes in the number, strength, and directionality of the synaptic connections among the billions of neurons. In artificial neural networks, learning and adaptation can take place by varying the number, weighting, and directionality of the input/output connections among engineered neurons. Click images to learn more about biotechnology/neurotechnology T-shirts: Questions and suggestions concerning the site NeuralComputers.us may be sent to: NeuralComputers.us © 2006 by NeuralComputers.us