Simulated brain scores top test marks : Nature News & Comment

Simulated brain scores top test marks : Nature News:  It stands apart from other attempts to simulate a brain, such as the ambitious Blue Brain Project (see 'Brain in a box'), because it produces complex behaviours with fewer neurons...


A pure computer simulation, Spaun simulates the physiology of each of its neurons, from spikes of electricity that flow through them to neurotransmitters that cross between them. The computing cells are divided into groups, corresponding to specific parts of the brain that process images, control movements and store short-term memories. These regions are wired together in a realistic way, and even respond to inputs that mimic the action of neurotransmitters.

As Spaun sees a stream of numbers, it extracts visual features so that it can recognize the digits. It can then perform at least eight different tasks, from simple ones like copying an image, to more complex ones similar to those found on IQ tests, such as finding the next number in a series. When finished, it writes out its answer with a physically modelled arm.

IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer | KurzweilAI

IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer | KurzweilAI: IBM and LBNL achieved an unprecedented scale of 2.084 billion neurosynaptic cores* containing 53×1010  (530 billion) neurons and 1.37×1014 (100 trillion) synapses running only 1542 times slower than real time.

“We have not built a biologically realistic simulation of the complete human brain,” explains an abstract....  “Computation (‘neurons’), memory (‘synapses’), and communication (‘axons,’ ‘dendrites’) are mathematically abstracted away from biological detail toward engineering goals of maximizing function (utility, applications) and minimizing cost (power, area, delay) and design complexity of hardware implementation.”

Blue Brain project accurately predicts connections between neurons

Blue Brain project accurately predicts connections between neurons: To their great surprise, they found that the locations on the model matched that of synapses found in the equivalent real-brain circuit with an accuracy ranging from 75 percent to 95 percent...

This means that neurons grow as independently of each other as physically possible and mostly form synapses at the locations where they randomly bump into each other.
A few exceptions were also discovered, pointing out special cases where signals are used by neurons to change the statistical connectivity. By taking these exceptions into account, the Blue Brain team can now make a near perfect prediction of the locations of all the synapses formed inside the circuit.

$1.3B 'Brain in a Box' Project Faces Skepticism: Scientific American

$1.3B 'Brain in a Box' Project Faces Skepticism: Scientific American: At the heart of that approach is Markram's conviction that a good unifying model has to assimilate data from the bottom up. In his view, modelers should start at the most basic level--he focuses on ion channels because they determine when a neuron fires--and get everything working at one level before proceeding to the next. This requires a lot of educated guesses, but Markram argues that the admittedly huge gaps in knowledge about the brain can be filled with data as experiments are published--the Blue Brain model is updated once a week. The alternative approach, approximating and abstracting away the biological detail, leaves no way to be sure that the model's behavior has anything to do with how the brain works, said Markram.

This is where other computational neuroscientists gnash their teeth. Most of them are already using simple models of individual neurons to explore high-level functions such as pattern recognition. Markram's bottom-up approach risks missing the wood for the trees, many of them argued in Bern: the model could be so detailed that it is no easier to understand than the real brain. And that is if Markram can build it at all. Judging by what Blue Brain has accomplished in the past six years, critics said, that seems unlikely. The tiny swathe of simulated rat cortex has no inputs from sensory organs or outputs to other parts of the brain, and produces almost no interesting behavior, pointed out Kevan Martin, co-director of the INI, in an e-mail. It is "certainly not the case" that Markram has simulated the column as it works in a whole animal, he said.

DVD alloys help make computers that think like us - tech - 12 July 2011 - New Scientist

DVD alloys help make computers that think like us: GST is known as a "phase-change" alloy, because of its ability to change its molecular structure from a crystalline to a disordered amorphous "phase" when heated...
Different areas within a tiny spot of GST can be crystalline or amorphous to differing degrees, which means it can store information across a much wider range of values than simply 0 or 1...
Wright's neuron is able to mimic this threshold firing because GST's electrical resistance drops suddenly when it moves from its amorphous phase to the crystalline. So incoming signals in the form of pulses of current are applied to the artificial neuron - and it is deemed to have fired when its resistance plummets...
GST's ability to change its resistance has allowed them to program it to dynamically modify the strength of the nanoscale artificial synapses they have built...
a system with 10^10 synapses would consume just 10 watts...

Henry Markram builds a brain in a supercomputer | Video on TED.com

Henry Markram builds a brain in a supercomputer | Video on TED.com: Discussion of the Blue Brain project

Army of smartphone chips could emulate the human brain

Called Spinnaker - for Spiking Neural Network Architecture - the brain is based on a processor created in 1987 by Furber and colleagues

http://www.newscientist.com/article/mg20627585.700-army-of-smartphone-chips-could-emulate-the-human-brain.html

Reverse engineering the brain

http://biomedicalcomputationreview.org/5/2/7.pdf

Out of the Blue � SEEDMAGAZINE.COM

Out of the Blue � SEEDMAGAZINE.COM: After assembling a three-dimensional model of 10,000 virtual neurons, the scientists began feeding the simulation electrical impulses, which were designed to replicate the currents constantly rippling through a real rat brain. Because the model focused on one particular kind of neural circuit—a neocortical column in the somatosensory cortex of a two-week-old rat—the scientists could feed the supercomputer the same sort of electrical stimulation that a newborn rat would actually experience.

It didn’t take long before the model reacted. After only a few electrical jolts, the artificial neural circuit began to act just like a real neural circuit. Clusters of connected neurons began to fire in close synchrony: the cells were wiring themselves together. Different cell types obeyed their genetic instructions. The scientists could see the cellular looms flash and then fade as the cells wove themselves into meaningful patterns. Dendrites reached out to each other, like branches looking for light. “This all happened on its own,” Markram says. “It was entirely spontaneous.” For the Blue Brain team, it was a thrilling breakthrough. After years of hard work, they were finally able to watch their make-believe brain develop, synapse by synapse. The microchips were turning themselves into a mind.