In 2009, tge Biomedical Computation Review looked at the status of work towards reverse engineering brain. (8 page pdf)
Computer simulations of the brain already allow experiments impossible to carry out with animals. “As good as modern neuroscience is—and it has been brilliant over the last two decades—we can’t really sample every neuron and every synapse as they are performing a behavior,” notes consciousness researcher Gerald Edelman, MD, PhD, director of the Neurosciences Institute and chair of neurobiology at the Scripps Research Institute in San Diego, California.
Researchers are looking to develop even more efficient simulated brains to help produce computers that can think while at the same time accelerating neuroscience. Ultimately brain simulations promise the ability to study the effect of drugs and disease and aid in the design of new therapeutic strategies.
To build a simulated brain, Edelman and others start with what’s known about the neuron, a cell that actively maintains a separation of charged ions across its membrane. Specific channels in the membrane allow certain ions in, and these are quickly pumped back out, or sequestered internally. But when a certain threshold of charge is reached the neuron fires a spike of current toward an adjacent neuron.
Here, at the synapse—a microscopic gap between each nerve cell—current becomes chemistry (and here is where drugs alter that chemistry). A spike wave arriving at the synapse triggers the release of neurotransmitters—to activate the next cell—provided enough inputs arrive in a very short time. Sufficient impulses strengthen the synapse. Neglected, the synaptic strength weakens and the particular listening in with electrodes a hundred times finer than a human hair. And this is the basic information that Edelman and others use to construct their simulated neurons.
To determine how these neurons are connected, simulators turn to microscopists and their latest technologies. Techniques from immunology have brought incredible resolution on the molecular level: cells containing particular molecules can be tagged by dye-bearing antibodies so that researchers can distinguish them from from fellows and follow their links to one another. Scanning electron microscopy has been able to home in on the fine molecular scale at the synapse.
Knowing how individual neurons function and how they’re connected will not make a brain work. Simulators need to know the bigger picture of brain area networks. To understand the function of brain regions, neuroscientists initially used data from scalp EEG and depth electrodes placed within the brains of living patients and animals, as well as observational reports such as from accidents that selectively damaged specific brain areas. These days computer-analyzed imaging can reveal additional details of the normal brain. Simulators employ all of these lines of evidence, and still seek more. But none of this data could produce an engineered brain without huge advances in computer simulation.
Once enough of the brain’s macro and microcircuitry is simulated, the in silico model is able to generate its own inherent activity—similar to what is seen in real brains. “When you stimulate the neural model, it takes off on its own and is constantly active,” Edelman says. “We’ve never succeeded in doing this before.” Moreover, oscillating waves of synchronous neural firing not explicitly built-in emerged spontaneously, the researchers reported in the March 4, 2008, Proceedings of the National Academy of Sciences. The researchers also were able to induce and reproduce spontaneous, low-level activity at the synapses—called miniature postsynaptic potentials or minis. The results suggest that, as a real brain
develops in a fetus, minis like these might prime neurons for action.
Edelman’s group relied on a top-down approach based on global network properties of the brain and mathematical formulas to reproduce known types of neuron behavior. In a complementary approach, Blue Brain focuses on exact structural and molecular details to model a particular piece of the brain, building up from exact details of individual neurons. Data for the Blue Brain project was gathered using a key innovation: the ability to record ion signals from many neurons at once using what’s called a multiple unit patch clamp technique. By eavesdropping on the interactions
among neurons, researchers learned what synaptic currents were being generated and where.
In addition, they gathered data on gene activity within neurons—as an indicator of which discrete ion channels are present. In most neurons, a dozen or more types of these pores regulate ion flow. The Blue Brain simulation specifies which ones are present in each neuron. They also captured the precise connecting points of each neuron, by injecting dye once they were done recording the electrical activity. “The details are accurate, down to the micron,” for each contact point of each nerve fiber, adds Phil Goodman, MD, professor of Internal Medicine and Biomedical Engineering at the University of Nevada, Reno, who collaborated on Blue Brain.
The Blue Brain project plans to publish “key insights never seen before in the neo-cortical column,” Markram says. “By the end of 2009 we will publish the entire
circuit with the blueprint. It’s like the genome map—it’s a comprehensive description of the neocortical column.” “It took 15 years to get the data for
this small piece of brain,” Markram says. “Every week the model becomes more biological,” he adds. “It’s very much like a real little bit of tissue.” And now that they’ve built one cortical column, building another is a simple task. “We can (now) push a button and build an unlimited amount of neurons automatically.”
Boahen and members of his Stanford lab have developed the Neurogrid chip. No
bigger than a fingernail, 16 of these chips will be assembled in an iPod-sized
device that can do what a supercomputer does—simulate a million neurons—at only $40,000. The Neurogrid chips have been received from the silicon foundry and should allow the group to emulate a million neurons in the cortex in real time at a thousandth of the cost of supercomputing.
Object recognition is vital for a virtual or a material brain-based device such as the Darwin series or Goodman’s avatars. Yet it has been one of the most challenging tasks for artificial intelligence. Goodman uses fairly primitive visual processing in his model, but Thomas Serre, PhD, a postdoc working with Tomaso Poggio, PhD, at the Massachusetts Institute of Technology, has recreated in a machine the ability
to perceive objects when flashed at the threshold of human visual perception.
Remarkably, the simulation performs as well as people (as described in a
News Byte in the Summer 2007 issue of Biomedical Computation Review
Serre’s experiment was limited, however, to the brain’s response to an image
flashed for less than 150 milliseconds. Thus, it provides just a skeleton of a
complete theory of vision, Serre says. He’s now working on what happens
beyond the first 150 milliseconds of visual processing—“when you move
your eyes and shift attention.” The visual system involves a complex of more than 30 brain areas propagating signals from the retina through the visual cortex to the region of motor cortex that controls how the person (or the simulator) responds. Living brain also contains back projections, echoing all the way back to the primary visual area that receives the initial signals from the retina. Vision researchers suspect these back connections may be the way that the visual system can pick
out a target object from complex scenes. “By adding back projections to the model, and allowing one shift in attention, to one part of the image, we are (now) able to mimic the next level of performance of a human observer when the image is left
just 30 ms longer on the screen, just enough for people to shift their attention once,” Serre says.
Boahen at Stanford heads a team working on recreating the basics of different parts of the perceiving brain. Much of the circuitry they plan to model will include back projections. Boahen agrees that feedback likely mediates attention, as competing firing is suppressed. As with other brain simulations, his also shows synchrony, the living rhythms of the brain, including gamma waves with attention.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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