I just finished reading Complexity: A Guided Tour by Melanie Mitchell. The book is meant to be an introduction to complexity theory for the general reader.

The book works as a lucid review of many interesting topics in science and mathematics. I’d read Mitchell’s book on genetic algorithms, and she’s a gifted writer. Here she explores (among other things) dynamical systems, chaos, information theory, genetic algorithms, cellular automata, analogical reasoning, and network theory. She does a great job explaining difficult concepts in a clear manner.

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“You think you know when you learn, are more sure when you can write, even more when you can teach, but certain when you can program.” Alan J. Perlis, Yale University

Neurdons cannot agree more. Reading and writing about neuroscience is not nearly as fun as creating a pulsing neural model! Recently, the Technology Lab at the Department of Cognitive and Neural Systems, where Neurdon was founded, has started to post a number of software tools, most of them in MATLAB, ranging from neural simulation software, to simple neural models, to biologically-inspired machine learning and machine vision tools. Read the rest of this entry »

All this Neurdon hullabaloo over memristors and Kurzweilian futurism has got me thinking about the inevitable media question concerning all this: Will our RoboSlave Bots learn to love us in a somewhat creepy, Haley Joel Osment “Artificial Intelligence: AI” kind of way? In other words, will humans be able to one day produce conscious, silicon-based offspring? There are obviously a cornucopia of contingencies when discussing artificial sentience, however, I am going to not-so-subtly sidestep all the philosophical snafus and approach the problem from a modeler’s POV. Read the rest of this entry »

I’ve thought a bit about how modelers approach brain areas whose functions are still not very well constrained by robust neurophysiological data. By this, I mean that there is simply not enough data to say, in plain terms, what that particular brain area does. In terms of visual cortex, this pretty much accounts for all areas beyond V1, namely V2, V3, V4, posterior IT (ITp), anterior IT (ITa), which all form a loose hierarchy (in the order they’re listed), and whatever areas of the temporal lobe may be ‘visual’, e.g. entorhinal. These words may sound a bit harsh, or even better, like flame-bait. Yet, when a major computationalist publishes an article titled “How Close Are We to Understanding V1?” (to be read in the accusatory sense), and one takes into account that V1 is supposed to be the one area neuroscience figured out decades ago, well, that changes things.

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A recent article on the WSJ (In Search for Intelligence, a Silicon Brain Twitches) reviews the Blue Brain project based at the École Polytechnique Fédérale de Lausanne in Switzerland. The Blue Brian project, led for the last four years by Henry Markram, has focused in building a biologically accurate rat cortical column. Read the rest of this entry »

Justin Mullins is the author of a nice post on the Memristor, appeard on 7/8/2009 on New Scientist. It does a nice job in describing the story of the memristor, from his theoretical discovery in 1971 by Leon Chua at the University of California, Berkeley, to his utilization by Stan Williams and Greg Snider at the HP Labs in Palo Alto, to the implementation of neural models, which involves the department that hosts the Neurdons!… Read the rest of this entry »