Khan Academy An Introduction to Neural Networks Prof. Leslie Smith Centre for Cognitive and Computational Neuroscience Department of Computing and Mathematics University of Stirling. lss@cs.stir.ac.uk last major update: 25 October 1996: minor update 22 April 1998 and 12 Sept 2001: links updated (they were out of date) 12 Sept 2001; fix to math font (thanks Sietse Brouwer) 2 April 2003 This document is a roughly HTML-ised version of a talk given at the NSYN meeting in Edinburgh, Scotland, on 28 February 1996, then updated a few times in response to comments received. Please email me comments, but remember that this was originally just the slides from an introductory talk! Why would anyone want a `new' sort of computer? What is a neural network? Some algorithms and architectures. Where have they been applied? What new applications are likely? Some useful sources of information. Some comments added Sept 2001 NEW: questions and answers arising from this tutorial Why would anyone want a `new' sort of computer? Good at Not so good at Fast arithmetic
<em>g</em>, a Statistical Myth g, a Statistical Myth Attention Conservation Notice: About 11,000 words on the triviality of finding that positively correlated variables are all correlated with a linear combination of each other, and why this becomes no more profound when the variables are scores on intelligence tests. Unlikely to change the opinion of anyone who's read enough about the area to have one, but also unlikely to give enough information about the underlying statistical techniques to clarify them to novices. Includes multiple simulations, exasperation, and lots of unwarranted intellectual arrogance on my part. Follows, but is independent of, two earlier posts on the subject of intelligence and its biological basis, and their own sequel on heritability and malleability. This doubtless more than exhausts your interest in reading about the subject; it has certainly exhausted my interest in writing about it. The origin of g: Spearman's original general factor theory The modern g (And it's not just me.
New Pattern Found in Prime Numbers (PhysOrg.com) -- Prime numbers have intrigued curious thinkers for centuries. On one hand, prime numbers seem to be randomly distributed among the natural numbers with no other law than that of chance. But on the other hand, the global distribution of primes reveals a remarkably smooth regularity. This combination of randomness and regularity has motivated researchers to search for patterns in the distribution of primes that may eventually shed light on their ultimate nature. In a recent study, Bartolo Luque and Lucas Lacasa of the Universidad Politécnica de Madrid in Spain have discovered a new pattern in primes that has surprisingly gone unnoticed until now. “Mathematicians have studied prime numbers for centuries,” Lacasa told PhysOrg.com. Benford’s law (BL), named after physicist Frank Benford in 1938, describes the distribution of the leading digits of the numbers in a wide variety of data sets and mathematical sequences. “BL is a specific case of GBL,” Lacasa explained.
Scientific Speed Reading: How to Read 300% Faster in 20 Minutes (Photo: Dustin Diaz) How much more could you get done if you completed all of your required reading in 1/3 or 1/5 the time? Increasing reading speed is a process of controlling fine motor movement—period. This post is a condensed overview of principles I taught to undergraduates at Princeton University in 1998 at a seminar called the “PX Project.” I have never seen the method fail. The PX Project The PX Project, a single 3-hour cognitive experiment, produced an average increase in reading speed of 386%. It was tested with speakers of five languages, and even dyslexics were conditioned to read technical material at more than 3,000 words-per-minute (wpm), or 10 pages per minute. If you understand several basic principles of the human visual system, you can eliminate inefficiencies and increase speed while improving retention. First, several definitions and distinctions specific to the reading process: You do not read in a straight line, but rather in a sequence of saccadic movements (jumps).
Hammack Home This book is an introduction to the standard methods of proving mathematical theorems. It has been approved by the American Institute of Mathematics' Open Textbook Initiative. Also see the Mathematical Association of America Math DL review (of the 1st edition), and the Amazon reviews. The second edition is identical to the first edition, except some mistakes have been corrected, new exercises have been added, and Chapter 13 has been extended. Order a copy from Amazon or Barnes & Noble for $13.75 or download a pdf for free here. Part I: Fundamentals Part II: How to Prove Conditional Statements Part III: More on Proof Part IV: Relations, Functions and Cardinality Thanks to readers around the world who wrote to report mistakes and typos! Instructors: Click here for my page for VCU's MATH 300, a course based on this book. I will always offer the book for free on my web page, and for the lowest possible price through on-demand publishing.
Brain size and evolution - complexity, "behavioral complexity", and brain size From Serendip Organisms have indeed gotten more "complex" over evolutionary time, at least on a broad scale Organisms differ in "behavioral complexity" Organisms differ in brain sizeThere is some relation between "behavioral complexity" and brain size, but humans do not have the largest brains. There is a better relation between "behavioral complexity" and brain size in relation to body size. from Harry J. I'm still more interested in whether there is life on Mars?.
The human brain can create structures in up to 11 dimensions Neuroscientists have used a classic branch of maths in a totally new way to peer into the structure of our brains. What they've discovered is that the brain is full of multi-dimensional geometrical structures operating in as many as 11 dimensions. We're used to thinking of the world from a 3-D perspective, so this may sound a bit tricky, but the results of this new study could be the next major step in understanding the fabric of the human brain - the most complex structure we know of. This latest brain model was produced by a team of researchers from the Blue Brain Project, a Swiss research initiative devoted to building a supercomputer-powered reconstruction of the human brain. The team used algebraic topology, a branch of mathematics used to describe the properties of objects and spaces regardless of how they change shape. "We found a world that we had never imagined," says lead researcher, neuroscientist Henry Markram from the EPFL institute in Switzerland.
Max Planck Neuroscience on Nautilus: Surprising Network Activity in the Immature Brain One of the outstanding mysteries of the cerebral cortex is how individual neurons develop the proper synaptic connections to form large-scale, distributed networks. Now, an international team of scientists have gained novel insights from studying spontaneously generated patterns of activity arising from local connections in the early developing visual cortex. These early activity patterns serve as a template for the subsequent development of the long-range neural connections that are a defining feature of mature distributed networks. In a recently published Nature Neuroscience article, scientists at the Max Planck Florida Institute for Neuroscience, Frankfurt Institute for Advanced Studies, Goethe University of Frankfurt, and the University of Minnesota detail how they investigated the visual cortex of the ferret, an ideal model system to explore the early development of networks in the cortex. Lead image credit: sdecoret / Shutterstock