what r neural networks?
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A neural network is an interconnected group of biological neurons. In modern usage the term can also refer to artificial neural networks, which are constituted of artificial neurons. Thus the term 'Neural Network' specifies two distinct concepts:\n\n   1. A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or the central nervous system. In the field of neuroscience, it most often refers to a group of neurons from a nervous system that are suited for laboratory analysis.\n   2. Artificial neural networks were designed to model some properties of biological neural networks, though most of the applications are of technical nature as opposed to cognitive models.
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As a librarian, I highly recommend you go to Scirus, a comprehensive online medical science encyclopedia, and search for "neural coding."\n\nI did a preliminary search and found 7,157 professional sources.\n\nVisit Scirus online at: http://www.scirus.com/srsapp/
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http://www.cs.ucdavis.edu/~haungs/paper/node11.html
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Yes:  theory of computational systems.
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In information technology, a neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships (for example, "A grandfather is older than a person's father"). A program can then tell the network how to behave in response to an external stimulus (for example, to input from a computer user who is interacting with the network) or can initiate activity on its own (within the limits of its access to the external world). \nIn making determinations, neural networks use several principles, including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are sometimes described in terms of knowledge layers, with, in general, more complex networks having deeper layers. In feedforward systems, learned relationships about data can "feed forward" to higher layers of knowledge. Neural networks can also learn temporal concepts and have been widely used in signal processing and time series analysis. \n\nCurrent applications of neural networks include: oil exploration data analysis, weather prediction, the interpretation of nucleotide sequences in biology labs, and the exploration of models of thinking and consciousness. In his novel, Galatea 2.2, Richard Powers envisioned a neural network (named "Helen") that could be taught to pass a comprehensive exam in English literature.
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The best open-source data mining tool is definitely WEKA.\n\nIt is possible to run 50 million (1 M records x 50 attributes) attribute values in WEKA. But you have to set maximum heap size for RAM to run it.\n\nPolyanalyst is probably for user-friendly for new data miners, but if become experienced, statistical packages like MATLAB (commerical) and R (open source) are much more flexible and better.\n\nI think it is always better to use free and well-known stuff before considering paying money for commercial ones :) My bet is on WEKA if you are going to do some form of classification.
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Here you go! Hope this helps.\n\nhttp://web.uvic.ca/~rburke/burkelab/neuraldevelopment.htm
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http://en.wikipedia.org/wiki/Data_mining
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numerical recipes in C.  Amazon has it.
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there's are so many different approach's....\n\ncitebase.eprints.org/cgi-bin/citations?id=oai:arXiv.org:nlin/0511015 \n\nhere's one site i'm sure you could link from there....
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DO NT GET R QUESTION!
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Prolog or Lisp. Machine learning\nMore detail please.\nI'm more for Java.\nBut most likely you are looking for the toolkit From MATLAB(tm) the first link.
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There is a great Cal Tech Video on that.  The one dealing with vectors.  It should be at most larger libraries.
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molecular memory and neuro net artificial intelligence.
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Hi, \n\nI think you are referring to the r-squared value of an equation that models a set of data. Often times when we plot a set of data, we want to find a relationship between the points that reveals an underlying relationship. In some cases, that relationship is linear, that is all the points lie roughly on a line.\n\nThe R-squared value tells you how well your line fits the points that you've plotted. It's simply a way of judging how good your approximation is.
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Newton\nEinstein\nDarwin\nPasteur
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http://www.pnas.org/cgi/content/full/102/19/7014