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A recent breakthrough in the development of an artificial synapse suggests that assistive devices and other prostheses won’t be limited to just missing joints and failing organs. Researchers in Japan have shown that it’s possible to mimic synaptic function with nanotechnology, a breakthrough that could result in not just artificial neural networks, but fixes for the human brain as well.
Synapses are essential to brain function. It’s what allows a neuron to pass an electric or chemical signal to another cell. Its structure is incredibly complex, with hundreds of proteins and other chemicals interacting in a complicated way. It’s because of this that cognitive scientists and artificial intelligence researchers have had great difficulty trying to simulate this exact function.
But a new study published in Advanced Functional Materials has shown that it may be possible to reproduce synaptic function by using a single nanoscale electrochemical atomic switch. Japanese researchers developed a tiny device that has a gap bridged by a copper filament under a voltage pulse stimulation. This results in a change in conductance which is time-dependant — a change in strength that’s nearly identical to the one found in biological synaptic systems. The inorganic synapses could thus be controlled by changes in interval, amplitude, and width of an input voltage pulse stimulation.
Why this is exciting is that the device is essentially mimicking the major features of human cognition, what the researchers refer to as the “emulation of synaptic plasticity”, including what goes on in short-term and long-term memory. Not only that, it responds to the presence of air and temperature changes, which indicates that it has the potential to perceive the environment much like the human brain.
The researchers are hoping that their newfound insight could help in the development of artificial neural networks, but it’s clear that their system, which operates at a microscopic level, could also be used to treat the human brain. The day may be coming when failing synaptic systems could be patched with a device similar to this one, in which biological function is offloaded to a synthetic one.
Pictured: A new brain map shows what happens when acupuncture points on the body are stimulated.
Originating in ancient China, acupuncture has been used for 2500 years. Traditional Chinese medicine holds that disease is caused by blockages and imbalances of energy (known as chi) flowing through meridians in the body, and can be eased by inserting needles at specific points.
Since the 1970s, acupuncture has become more popular outside east Asia. Once widely considered a quack medicine, there is now tentative support for its use in certain conditions from respected official bodies such as the World Health Organization, the National Health Service in the UK and the National Institutes of Health in the US.
There is evidence that acupuncture is effective in treating a range of conditions including spinal injuries, infertility and the side effects of chemotherapy , and that its effects aren’t entirely due to the placebo effect. However, despite extensive research, the mechanism of this ancient healing art remains unknown.
“You are an ever-morphing 4 dimensional fractal.”
Female Orgasm Captured In Series of Brain Scans
Scientists have used brain scan images to create the world’s first movie of the female brain as it approaches, experiences and recovers from an orgasm. The animation reveals the steady buildup of activity in the brain as disparate regions flicker into life and then come together in a crescendo of activity before gently settling back down again.
To make the animation, researchers monitored a woman’s brain as she lay in a functional magnetic resonance imaging (fMRI) scanner and stimulated herself. The research will help scientists to understand how the brain conducts the symphony of activity that leads to sexual climax in a woman.
By studying people who have orgasms, Professor Barry Komisaruk, a psychologist at Rutgers University in New Jersey and his team hope to uncover what goes wrong in both men and women who cannot reach sexual climax.
The above video displays fMRI images of a woman’s brain as she experiences an orgasm. Oxygen levels in the blood correspond to the activity of different brain regions and are represented here on a spectrum from dark red (lowest) to yellow/white (highest). Twenty snapshots of the data have been taken from a 12-minute sequence during which she approaches orgasm, achieves orgasm and then enters a refractory period.
An excellent read. See more here.
If the human brain – with 100 billion neurons forging trillions of connections – were not complicated enough, new research suggests that every neuron may have its very own genome.
A study of the genomes of individual human neurons created from reprogrammed stem cells reveals huge variability between neurons from the same person. Such variation could explain differences in behaviour and susceptibility to mental illness, says Mike McConnell, a stem cell biologist at the Salk Institute in La Jolla California. He presented the work 13 October at the Society for Neuroscience conference in Washington D.C.
“Monozygotic twins can, from time to time, be discordant for things like schizophrenia, for things like autism. They grew up together. They have the same genome, why are they different,” he says.
McConnell has been exploring this phenomenon for more than a decade. In a 2001 paper, he and his colleagues found that individual mouse cells destined to develop into neurons contain substantial chromosomal changes called aneuploidy. A few years later, he showed that neurons with these changes are active in the mouse brain.
Because humans are born with most of the neurons they will use throughout life, genetic variability among them could have a long-lasting effect on how people behave, McConnell says.
To see if human brain cells are genetic mosaics, McConnell turned to induced pluripotent stem (iPS) cells. They are created by treating adult cells with a suite of reprogramming factors that transform the cells into an embryonic-like state in which they can form other tissues.His team transformed iPS cells from two people into neuron cells and then examined the genomes of individual neurons, looking for places where the a huge chunk of the genome is missing or duplicated.
No brain cell’s genome looked the same. They all contained numerous duplications and deletions, but never the same pattern. His team also examined the genomes of the adult cells that were reprogrammed into iPS cells and then neurons, and these cells contained numerous insertions and deletion, but not the same ones as the neurons. McConnell says that this suggests that cells acquire their own genomes as they turn into neurons.
Right now, McConnell can only speculate on whether these changes might influence behaviour. But he is eager to go looking for signs of genetic mosaicism in real human brains, as well as reprogrammed neurons from people with conditions such as schizophrenia.
The sorts of genetic changes he found in the mosaic neurons – deletions and duplications – have been linked, in rare cases, to neuropsychiatric and neurodevelopmental diseases such as schizophrenia, bipolar syndrome and autism. Geneticists discovered these mutations in the germ lines of people with these conditions, i.e. every cell in their body contains the mutations. But McConnell hypothesizes that these mutations could also form in individual neurons in the developing brain.
Add McConnell’s observation to the overwhelming evidence that epigenetic modifications influence traits such as obesity, or the suggestion that the sequences of many genes are subtlty altered after being transcribed, and you get the sense that organism are not content to stick with the genome they were born with.
Habits may be difficult to change, but now at least we have an insight into how they form.
When a group of neurons fire simultaneously, the activity appears as a brainwave. Different brainwave-frequencies are linked to different tasks in the brain.
To track how brainwaves change during learning, Ann Graybiel and Mark Howe at the Massachusetts Institute of Technology used electrodes to analyse brainwaves in the ventromedial striatum of rats while they were taught to navigate a maze.
As rats were learning the task their brain activity showed bursts of fast gamma waves. Once the rats mastered the task, their brainwaves slowed to almost a quarter of their initial frequency, becoming beta waves. Graybiel’s team suspects this transition reflects when learning becomes habit.
Graybiel says the slower brainwaves may be the brain weeding out excess activity to refine behaviour. She suggests it might be possible to boost the rate at which you learn a skill by enhancing such beta-wave activity.
(via New Scientist)
BLUEBRAIN | Year One
Henry Markram is attempting to reverse engineer an entire human brain, one neuron at a time. This piece is an introduction to director Noah Hutton’s 10-year film-in-the-making that will chronicle the development of The Blue Brain Project, a landmark endeavor in modern neuroscience.
Since it’ll be the first one I legitimately share outside of the bedroom… I’d be happy for some suggestions. I’m at a loss. Something chill and soothing I would imagine… any suggestions?
I know I have tons of possible options, yet my mind draws a blank. I just want it to be the best it can be.
Update: Thank you guys, so much! Bonobo and Nujabes are definitely viable choices! Safe to say there will also be some Burial in the mix. Keep the suggestions coming.
Jorge De la Paz (Chile) - Curioos
ScienceDaily (July 20, 2011) — Artificial intelligence has been the inspiration for countless books and movies, as well as the aspiration of countless scientists and engineers. Researchers at the California Institute of Technology (Caltech) have now taken a major step toward creating artificial intelligence — not in a robot or a silicon chip, but in a test tube. The researchers are the first to have made an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete patterns, just as a brain can.
Manchester academics aim to use a million ARM processing cores to simulate the neuron network of the human brain and investigate new models of computing.
The bedrock of the SpiNNaker computing architecture is formed of 50,000 or so ARM 968-series multi-core, low-powered embedded processors, which passed their functionality tests “with flying colours”, Manchester University said on Thursday.
"The most fundamental deliverable from this project is a generic computing platform that can be used to test hypotheses that are emerging from psychology and neuroscience about how information flows through the brain," Steve Furber, Manchester University’s ICL processor of computer engineering and leader of the project, told ZDNet UK.
Furber also hopes that by closely approximating the structure of the brain, the researchers will investigate more distributed and resilient computer systems. “At the moment, the way we build computers is not able to cope with component failure, but the brain does. We don’t know how to design things with that resilience,” he said. Furber helped design the Advanced RISC Machine (ARM) 32-bit processor while at Acorn in the 1980s, before ARM was spun-off as a separate company.
Eventually, the chips will form a supercomputer built out of a SpiNNaker — spiking neural network — architecture, in which each chip sits within a two-dimensional mesh network connected to six or so others. Each processor has 18 cores and around 100 million transistors, and is attached to 128 megabytes of DRAM, which has a billion transistors. A single Intel Sandy Bridge-based Core i5-750 processor has 774 million transistors. Intel’s server and supercomputing processor, the Xeon Nehalem-EX, has around 2.3 billion transistors.
Once built, the computer will be accessible to other academics and researchers via the internet, possibly through the UK’s research network Janet, Furber said.
At the moment, the researchers are testing the system with a card containing four ARM processors, giving 72 cores in total; they then hope to expand this and build a card-based system of 1,000 cores. By the end of the year the researchers hope to assemble a SpiNNaker architecture with 10,000 cores and anticipate achieving a million cores by the end of 2012, Furber said.
Each chip will mimic the spikes that neurons produce when they pass information between one another. “A spike is basically a fixed-energy impulse, so what you need to communicate is which neuron spiked and when it spiked,” Furber said. “When a processor that’s modelling a neuron computes that that neuron should spike it drops a 32-bit identifier into a 40-bit packet that goes into the local fabric”, at which point an on-chip router steers the packet to where it must go, Furber explained.
Because any processor can be turned into any particular neuron, the entire supercomputer can be modified, so while it can only simulate around one percent of the human brain, it can be modified to simulate different parts for other experiments.
"Imagine our machine as a giant FPGA [field-programmable gate array] where the individual components are not logic gates, but are neurons,” Furber explained. “Configuring a big machine is a significant software challenge, which we are working our way up towards.”
To that end, the researchers have ported a variant of high-level programming language Python to work on the SpiNNaker architecture.
A complete simulation of the entire brain is still far off. In June, a French academic predicted that a digital brain would be possible by around 2023.
Furber estimates the SpiNNaker architecture has a rough scale limit of around four billion neurons, compared to the 100 billion in the human brain, but with research this barrier could be broken.
"The real limit from our point of view is the kind of research budget that we can expect to get as a university research group," Furber said. Additionally, he feels that the community SpiNNaker is targeted at, such as neuroscientists and psychologists, would not "sensibly be able to exploit" a whole brain model because understanding of this part of the brain is patchy.
"In the cortex there are tens of different types of neurons and they interconnect with each other in specific ways and information about the ways they connect and the strength is almost nonexistent," he said.
The project has been funded by a £5m grant from the EPSRC, of which Manchester received £2.5m, with the rest going to the universities of Southampton, Cambridge and Sheffield. Additionally, Manchester has received some further small grants for the project, and an earlier grant of £750,000.
The researchers chose to use ARM because of Furber’s familiarity with the architecture and the relatively low power consumption — one watt per processor — of the ARM 968 chips.