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Posts Tagged ‘brain mapping’

Researchers from Columbia University now say they’ve identified these neuron groups, and have built a map of the “gustatory cortex.” It’s the first map showing how taste is represented in the mammalian brain.

A team led by neuroscientist Charles Zuker used a technique called two-photon calcium imaging to monitor neural activity that was sparked by “tastants,” as they put it. (In an exciting coincidence, Zuker’s name, if spelled with a “ck,” is the German word for sugar.)

Calcium spikes when a neuron is activated, so calcium increase can be used as a proxy for neuron activity, as a news release from Howard Hughes Medical Institute explains. In this manner, the team identified specific clusters of neurons that activated in response to droplets placed on the tongues of sedated mice. The neuron hot spots represented four of the five taste groups: saltiness, sweetness, bitterness and umami, or savoriness.

These neural clusters fired in different mice over successive tests, the researchers say. The only hot spot they could not find corresponded to sour taste. This could mean it’s outside of the cortical regions they studied, or maybe the anesthesia used to sedate the mice had some interference.

Other researchers have argued that taste works more like smell, in which olfactory sensors respond in a pattern that translates the scents that are present. In that scenario, taste cells would be tuned to a broader spectrum of inputs, and would determine tastes depending on the patterns of stimulation. The absence of a sour hot spot lends credence to this theory. But Zuker believes that’s not the case, and that his team’s more detailed imaging analysis was able to separate the neural hot spots, whereas previous studies seemed to clump them together.

Next, he wants to study how taste combines with other sensory inputs like smell and touch — and with hunger and anticipation — to provide a more well-rounded experience of taste.

The work was published today in Science.

[Science]

IBM, with help from DARPA, has built two working prototypes of a "neurosynaptic chip." Based on the neurons and synapses of the brain, these first-generation cognitive computing cores could represent a major leap in power, speed and efficiency

About a year ago, we told you about IBM’s project to map the neural circuitry of a macaque, the most complex brain networking project of its kind. Big Blue wasn’t doing it just for the sake of science — the goal was to reverse-engineer neural networks, helping pave the way to cognitive computer systems that can think as efficiently as the brain. Now they’ve made just such a system — two, actually — and they’re calling them neurosynaptic chips.

Built on 45 nanometer silicon/metal oxide semiconductor platform, both chips have 256 neurons. One chip has 262,144 programmable synapses and the other contains 65,536 learning synapses — which can remember and learn from their own actions. IBM researchers have used the compute cores for experiments in navigation, machine vision, pattern recognition, associative memory and classification, the company says. It’s a step toward redefining computers as adaptable, holistic learning systems, rather than yes-or-no calculators.

“This new architecture represents a critical shift away form today’s traditional von Neumann computers, to extremely power-efficient architecture,” Dharmendra Modha, project leader for IBM Research, said in an interview. “It integrates memory with processors, and it is fundamentally massively parallel and distributed as well as event-driven, so it begins to rival the brain’s function, power and space.”

You can read up on Von Neumann architecture over here, but essentially it is a system with two data portals, which are shared by the input instructions and output data. This creates a bottleneck that will fundamentally limit the speed of memory transfer. IBM’s system eliminates that bottleneck by putting the circuits for data computation and storage together, allowing the system to compute information from multiple sources at the same time with greater efficiency. Also like the brain, the chips have synaptic plasticity, meaning certain regions can be reconfigured to perform tasks to which they were not initially assigned.

IBM’s long-term goal is to build a chip system with 10 billion neurons and 100 trillion synapses that consumes just one kilowatt-hour of electricity and fits inside a shoebox, Modha said.

The project is funded by DARPA’s SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) initiative, and IBM just completed phases 0 and 1. IBM’s project, which involves collaborators from Columbia University, Cornell University, the University of California-Merced and the University of Wisconsin-Madison, just received another $21 million in funding for phase 2, the company said.

Computer scientists have been working for some time on systems that can emulate the brain’s massively parallel, low-power computing prowess, and they’ve made several breakthroughs. Last year, computer engineer Steve Furber described a synaptic computer network that consists of tens of thousands of cellphone chips.

The most notable computer-brain achievements have been in the field of memristors. As their name implies, a memory resistor can “remember” the last resistance that it possessed when current was flowing through it — so after current is turned back on, the resistance of the circuit will be the same. We will not attempt to delve too deeply here, but this basically makes a system much more efficient.

Hewlett-Packard has been developing memristors since first describing them in 2008, and has also been part of the SyNAPSE project. Last spring, HP engineers described a titanium dioxide memristor that uses low power.

For a brain-based computer system, memristors can function as a computer analogue for a synapse, which also stores information about previous data transfer. IBM's chip doesn't use a memristor architecture, but it does integrate memory with computation power — and it uses computer neurons and axons to do it. The building blocks are simple, but the architecture is unique, said Rajit Manohar, associate dean for research and graduate studies in the engineering school at Cornell.

"When a neuron changes its state, the state it is modifying is its own state, not the state of something else. So you can physically co-locate the circuit to do the computation, and the circuit to store the state. They can be very close to each other, so that cooperation becomes very efficient," he said.

Modha said it is just a new way to store memory.

"A bit is a bit is a bit. You could store a bit in a memristor, or a phase-change memory, or a nano-electromechanical switch, or SRAM, or any form of memory that you please. But by itself, that does not a complete architecture make," Modha said. "It has no computational capability."

But this new chip does have that power, he said. It integrates memory with processor capability on a typical SOI-CMOS platform, using traditional transistors in a new design. Along with integrated memory to stand in for synapses, the neurosynaptic “core” uses typical transistors for input-output capability, i.e. neurons.

This new architecture will not replace traditional computers, however. “Both will be with us for a long time to come, and continue to serve humanity,” Modha predicted.

The idea is that future powerful chips based on this brain-network design will be able to ingest and compute information from multiple inputs and make sense of it all — just like the brain does.

A cognitive computer monitoring the oceans could record and compute variables like temperature, wave height and acoustics, and decide whether to issue tsunami or hurricane warnings. Or a grocer stocking shelves could use a special glove that monitors scent, texture and sight to flag contaminated produce, Modha said. Modern computers can’t handle that level of detail from so many inputs, he said. But our brains do it all the time — grab a rotting peach, and your senses of touch, smell and sight work in concert instantaneously to determine that the fruit is bad.

To do this, the brain uses electrical signals between some 150 trillion synapses, all while sipping energy — our brains need about 20 watts to function. Understanding how this works is key to building brain-based computers, which is why IBM has been working with neuroscientists to study monkey and cat brains. That research is progressing, Modha said.

But it will be quite some time before computer chips can truly match the ultra-efficient computational powerhouses that nature gave us.

The idea behind the brain atlas is to develop a tool that researchers can access to determine how the genome is expressed in the brain, a process which is--needless to say--complex. Over four years, the ABI crunched more than 100 million data points to pinpoint 1,000 different anatomical sites in the brain that exhibit particular gene expression.

And for good measure--and comparability purposes--they did it with two adult human brains so scientists can see not only how genes are expressed in a particular brain, but the places where the human brain is genetically identical and where differences in genomes are expressed differently in the brain.

That last part is key for research purposes. Clinicians and researchers trying to zero in on the cause of a certain neurological condition or refine the search for a treatment can use the atlas to better understand how a treatment might work or how a mental illness or condition manifests itself.

As the genome becomes increasingly better understood and particular genes are isolated as the causes or indicators of certain disease expressions, anatomical models like the Allen Brain Atlas could go a long way toward helping researchers make the connection between the genome and the physical brain, using data they wouldn’t otherwise have access to. Less time spent connecting all the dots means more time spent looking for the right therapeutic solutions.

[Allen Institute for Brain Science]

The team consists of 33 researchers at nine different institutions, including Washington University School of Medicine in St. Louis and the University of Minnesota, the lead universities in the effort and the sites where much of the brain-scanning will take place. Their success will depend in part on another HCP grant to another research consortium headed up by Massachusetts General Hospital and UCLA that will develop advanced, custom brain scanners with higher spatial resolution and increased sensitivity. The funds themselves come from various bodies within the National Institutes of Health.

How big is the project? It’s at least 90 billion neurons big, but that doesn’t even convey the enormity and complexity of the human brain. There are something like 150 trillion synapses – the connections between neurons across which signals pass – that electrical signals must negotiate. These neurons and the connections between them make up the circuitry of the brain, and the HCP aims to create a better picture of that circuitry than we’ve ever had before.

The project aims to tap state-of-the-art brain scanning technologies, including diffusion imaging, various MRI methods, and magnetoencephalography to map not just how messages move through the brain, but how various regions work together via networks and networks of networks to achieve the complexity that is the human mind. With map resolutions down to the voxel – small swaths of grey matter containing about one million neurons each – researchers estimate the HCP will generate about one petabyte of data, which will require its own supercomputer to process.

All that scanning, data gathering, and analysis should pay off though, HCP researchers say. The end result will be an open platform that other neuroscientists can use to test their own theories, hypotheses, and findings against. Such a map should help scientists find their way to deeper understandings of how the brain works as well as cures for complicated neurological disorders.

[Medical Daily]

A significant stride towards reverse-engineering the darn thing

Focusing on a long-distance network connecting 383 brain regions and 6,602 long-distance connections that function like highways to connect disparate regions of the brain. Shorter, more localized connections were found to carry signals within regions.

But most importantly, they found what they describe in a paper published in PNAS as a "tightly integrated core" that might be they key to cognition in higher-thinking biological creatures. That core might be what gives us consciousness (we won't get into the philosophical implications there). Further, the core isn't located in one, or even two regions. The researchers found it stretches through the premotor cortex, prefrontal cortex, temporal lobe, thalamus, visual cortex and a handful of other regions.

Another surprising find: the prefrontal cortex, though at the front of the brain, might actually serve as its central information hub that distributes information throughout the brain.

The study included mapping of four times as many regions and three times the number of connections than the largest previous attempt. Those findings could finally help researchers mimic the brain -- which, even in this seemingly advanced era, is something of a mystery to us. That in turn could lead to network architecture and computer chips that process and move information as quickly and seamlessly as our brains do.

[Kurzweil AI]