Category: Life Sciences


Scientists Just Found Evidence That Neurons Can Communicate in a Way We Never Anticipated

By Hugo Angel,

Andrii Vodolazhskyi/Shutterstock.com

A new brain mechanism hiding in plain sight. Researchers have discovered a brand new mechanism that controls the way nerve cells in our brain communicate with each other to regulate learning and long-term memory.

The fact that a new brain mechanism has been hiding in plain sight is a reminder of how much we have yet to learn about how the human brain works, and what goes wrong in neurodegenerative disorders such as Alzheimer’s and epilepsy.

These discoveries represent a significant advance and will have far-reaching implications for the understanding of 

  • memory, 
  • cognition, 
  • developmental plasticity, and 
  • neuronal network formation and stabilisation,”  

said lead researcher Jeremy Henley from the University of Bristol in the UK.

We believe that this is a groundbreaking study that opens new lines of inquiry which will increase understanding of the molecular details of synaptic function in health and disease.

The human brain contains around 100 billion nerve cells, and each of those makes about 10,000 connections – known as synapses – with other cells.

That’s a whole lot of connections, and each of them is strengthened or weakened depending on different brain mechanisms that scientists have spent decades trying to understand.

Until now, one of the best known mechanisms to increase the strength of information flow across synapses was known as LTP, or long-term potentiation.

LTP intensifies the connection between cells to make information transfer more efficient, and it plays a role in a wide range of neurodegenerative conditions –  

  • too much LTP, and you risk disorders such as epilepsy,  
  • too little, and it could cause dementia or Alzheimer’s disease.
As far as researchers were aware, LTP is usually controlled by the activation of special proteins called NMDA receptors.

But now the UK team has discovered a brand new type of LTP that’s regulated in an entirely different way.

After investigating the formation of synapses in the lab, the team showed that this new LTP mechanism is controlled by molecules known as kainate receptors, instead of NMDA receptors.

These data reveal a new and, to our knowledge, previously unsuspected role for postsynaptic kainate receptors in the induction of functional and structural plasticity in the hippocampus,the researchers write in Nature Neuroscience.

This means we’ve now uncovered a previously unexplored mechanism that could control learning and memory.

Untangling the interactions between the signal receptors in the brain not only tells us more about the inner workings of a healthy brain, but also provides a practical insight into what happens when we form new memories,said one of the researchers, Milos Petrovic from the University of Central Lancashire.

If we can preserve these signals it may help protect against brain diseases.

Not only does this open up a new research pathway that could lead to a better understanding of how our brains work, but if researchers can find a way to target these new pathways, it could lead to more effective treatments for a range of neurodegenerative disorders.

It’s still early days, and the discovery will now need to be verified by independent researchers, but it’s a promising new field of research.

This is certainly an extremely exciting discovery and something that could potentially impact the global population,said Petrovic.

The research has been published in Nature Neuroscience.

ORIGINAL: IFLScience

By FIONA MACDONALD
20 FEB 2017

First Human Tests of Memory Boosting Brain Implant—a Big Leap Forward

By Hugo Angel,

You have to begin to lose your memory, if only bits and pieces, to realize that memory is what makes our lives. Life without memory is no life at all.” — Luis Buñuel Portolés, Filmmaker
Image Credit: Shutterstock.com
Every year, hundreds of millions of people experience the pain of a failing memory.
The reasons are many:

  • traumatic brain injury, which haunts a disturbingly high number of veterans and football players; 
  • stroke or Alzheimer’s disease, which often plagues the elderly; or 
  • even normal brain aging, which inevitably touches us all.
Memory loss seems to be inescapable. But one maverick neuroscientist is working hard on an electronic cure. Funded by DARPA, Dr. Theodore Berger, a biomedical engineer at the University of Southern California, is testing a memory-boosting implant that mimics the kind of signal processing that occurs when neurons are laying down new long-term memories.
The revolutionary implant, already shown to help memory encoding in rats and monkeys, is now being tested in human patients with epilepsy — an exciting first that may blow the field of memory prosthetics wide open.
To get here, however, the team first had to crack the memory code.

Deciphering Memory
From the very onset, Berger knew he was facing a behemoth of a problem.
We weren’t looking to match everything the brain does when it processes memory, but to at least come up with a decent mimic, said Berger.
Of course people asked: can you model it and put it into a device? Can you get that device to work in any brain? It’s those things that lead people to think I’m crazy. They think it’s too hard,” he said.
But the team had a solid place to start.
The hippocampus, a region buried deep within the folds and grooves of the brain, is the critical gatekeeper that transforms memories from short-lived to long-term. In dogged pursuit, Berger spent most of the last 35 years trying to understand how neurons in the hippocampus accomplish this complicated feat.
At its heart, a memory is a series of electrical pulses that occur over time that are generated by a given number of neurons, said Berger. This is important — it suggests that we can reduce it to mathematical equations and put it into a computational framework, he said.
Berger hasn’t been alone in his quest.
By listening to the chatter of neurons as an animal learns, teams of neuroscientists have begun to decipher the flow of information within the hippocampus that supports memory encoding. Key to this process is a strong electrical signal that travels from CA3, the “input” part of the hippocampus, to CA1, the “output” node.
This signal is impaired in people with memory disabilities, said Berger, so of course we thought if we could recreate it using silicon, we might be able to restore — or even boost — memory.

Bridging the Gap
Yet this brain’s memory code proved to be extremely tough to crack.
The problem lies in the non-linear nature of neural networks: signals are often noisy and constantly overlap in time, which leads to some inputs being suppressed or accentuated. In a network of hundreds and thousands of neurons, any small change could be greatly amplified and lead to vastly different outputs.
It’s a chaotic black box, laughed Berger.
With the help of modern computing techniques, however, Berger believes he may have a crude solution in hand. His proof?
Use his mathematical theorems to program a chip, and then see if the brain accepts the chip as a replacement — or additional — memory module.
Berger and his team began with a simple task using rats. They trained the animals to push one of two levers to get a tasty treat, and recorded the series of CA3 to CA1 electronic pulses in the hippocampus as the animals learned to pick the correct lever. The team carefully captured the way the signals were transformed as the session was laid down into long-term memory, and used that information — the electrical “essence” of the memory — to program an external memory chip.
They then injected the animals with a drug that temporarily disrupted their ability to form and access long-term memories, causing the animals to forget the reward-associated lever. Next, implanting microelectrodes into the hippocampus, the team pulsed CA1, the output region, with their memory code.
The results were striking — powered by an external memory module, the animals regained their ability to pick the right lever.
Encouraged by the results, Berger next tried his memory implant in monkeys, this time focusing on a brain region called the prefrontal cortex, which receives and modulates memories encoded by the hippocampus.
Placing electrodes into the monkey’s brains, the team showed the animals a series of semi-repeated images, and captured the prefrontal cortex’s activity when the animals recognized an image they had seen earlier. Then with a hefty dose of cocaine, the team inhibited that particular brain region, which disrupted the animal’s recall.
Next, using electrodes programmed with the “memory code,” the researchers guided the brain’s signal processing back on track — and the animal’s performance improved significantly.
A year later, the team further validated their memory implant by showing it could also rescue memory deficits due to hippocampal malfunction in the monkey brain.

A Human Memory Implant
Last year, the team cautiously began testing their memory implant prototype in human volunteers.
Because of the risks associated with brain surgery, the team recruited 12 patients with epilepsy, who already have electrodes implanted into their brain to track down the source of their seizures.
Repeated seizures steadily destroy critical parts of the hippocampus needed for long-term memory formation, explained Berger. So if the implant works, it could benefit these patients as well.
The team asked the volunteers to look through a series of pictures, and then recall which ones they had seen 90 seconds later. As the participants learned, the team recorded the firing patterns in both CA1 and CA3 — that is, the input and output nodes.
Using these data, the team extracted an algorithm — a specific human “memory code” — that could predict the pattern of activity in CA1 cells based on CA3 input. Compared to the brain’s actual firing patterns, the algorithm generated correct predictions roughly 80% of the time.
It’s not perfect, said Berger, but it’s a good start.
Using this algorithm, the researchers have begun to stimulate the output cells with an approximation of the transformed input signal.
We have already used the pattern to zap the brain of one woman with epilepsy, said Dr. Dong Song, an associate professor working with Berger. But he remained coy about the result, only saying that although promising, it’s still too early to tell.
Song’s caution is warranted. Unlike the motor cortex, with its clear structured representation of different body parts, the hippocampus is not organized in any obvious way.
It’s hard to understand why stimulating input locations can lead to predictable results, said Dr. Thoman McHugh, a neuroscientist at the RIKEN Brain Science Institute. It’s also difficult to tell whether such an implant could save the memory of those who suffer from damage to the output node of the hippocampus.
That said, the data is convincing,” McHugh acknowledged.
Berger, on the other hand, is ecstatic. “I never thought I’d see this go into humans,” he said.
But the work is far from done. Within the next few years, Berger wants to see whether the chip can help build long-term memories in a variety of different situations. After all, the algorithm was based on the team’s recordings of one specific task — what if the so-called memory code is not generalizable, instead varying based on the type of input that it receives?
Berger acknowledges that it’s a possibility, but he remains hopeful.
I do think that we will find a model that’s a pretty good fit for most conditions, he said. After all, the brain is restricted by its own biophysics — there’s only so many ways that electrical signals in the hippocampus can be processed, he said.
The goal is to improve the quality of life for somebody who has a severe memory deficit,” said Berger. “If I can give them the ability to form new long-term memories for half the conditions that most people live in, I’ll be happy as hell, and so will be most patients.
ORIGINAL: Singularity Hub

Research on largest network of cortical neurons to date published in Nature

By Hugo Angel,

Robust network of connections between neurons performing similar tasks shows fundamentals of how brain circuits are wired
Even the simplest networks of neurons in the brain are composed of millions of connections, and examining these vast networks is critical to understanding how the brain works. An international team of researchers, led by R. Clay Reid, Wei Chung Allen Lee and Vincent Bonin from the Allen Institute for Brain Science, Harvard Medical School and Neuro-Electronics Research Flanders (NERF), respectively, has published the largest network to date of connections between neurons in the cortex, where high-level processing occurs, and have revealed several crucial elements of how networks in the brain are organized. The results are published this week in the journal Nature.
A network of cortical neurons whose connections were traced from a multi-terabyte 3D data set. The data were created by an electron microscope designed and built at Harvard Medical School to collect millions of images in nanoscopic detail, so that every one of the “wires” could be seen, along with the connections between them. Some of the neurons are color-coded according to their activity patterns in the living brain. This is the newest example of functional connectomics, which combines high-throughput functional imaging, at single-cell resolution, with terascale anatomy of the very same neurons. Image credit: Clay Reid, Allen Institute; Wei-Chung Lee, Harvard Medical School; Sam Ingersoll, graphic artist
This is a culmination of a research program that began almost ten years ago. Brain networks are too large and complex to understand piecemeal, so we used high-throughput techniques to collect huge data sets of brain activity and brain wiring,” says R. Clay Reid, M.D., Ph.D., Senior Investigator at the Allen Institute for Brain Science. “But we are finding that the effort is absolutely worthwhile and that we are learning a tremendous amount about the structure of networks in the brain, and ultimately how the brain’s structure is linked to its function.
Although this study is a landmark moment in a substantial chapter of work, it is just the beginning,” says Wei-Chung Lee, Ph.D., Instructor in Neurobiology at Harvard Medicine School and lead author on the paper. “We now have the tools to embark on reverse engineering the brain by discovering relationships between circuit wiring and neuronal and network computations.” 
For decades, researchers have studied brain activity and wiring in isolation, unable to link the two,” says Vincent Bonin, Principal Investigator at Neuro-Electronics Research Flanders. “What we have achieved is to bridge these two realms with unprecedented detail, linking electrical activity in neurons with the nanoscale synaptic connections they make with one another.
We have found some of the first anatomical evidence for modular architecture in a cortical network as well as the structural basis for functionally specific connectivity between neurons,” Lee adds. “The approaches we used allowed us to define the organizational principles of neural circuits. We are now poised to discover cortical connectivity motifs, which may act as building blocks for cerebral network function.
Lee and Bonin began by identifying neurons in the mouse visual cortex that responded to particular visual stimuli, such as vertical or horizontal bars on a screen. Lee then made ultra-thin slices of brain and captured millions of detailed images of those targeted cells and synapses, which were then reconstructed in three dimensions. Teams of annotators on both coasts of the United States simultaneously traced individual neurons through the 3D stacks of images and located connections between individual neurons.
Analyzing this wealth of data yielded several results, including the first direct structural evidence to support the idea that neurons that do similar tasks are more likely to be connected to each other than neurons that carry out different tasks. Furthermore, those connections are larger, despite the fact that they are tangled with many other neurons that perform entirely different functions.
Part of what makes this study unique is the combination of functional imaging and detailed microscopy,” says Reid. “The microscopic data is of unprecedented scale and detail. We gain some very powerful knowledge by first learning what function a particular neuron performs, and then seeing how it connects with neurons that do similar or dissimilar things.
It’s like a symphony orchestra with players sitting in random seats,” Reid adds. “If you listen to only a few nearby musicians, it won’t make sense. By listening to everyone, you will understand the music; it actually becomes simpler. If you then ask who each musician is listening to, you might even figure out how they make the music. There’s no conductor, so the orchestra needs to communicate.
This combination of methods will also be employed in an IARPA contracted project with the Allen Institute for Brain Science, Baylor College of Medicine, and Princeton University, which seeks to scale these methods to a larger segment of brain tissue. The data of the present study is being made available online for other researchers to investigate.
This work was supported by the National Institutes of Health (R01 EY10115, R01 NS075436 and R21 NS085320); through resources provided by the National Resource for Biomedical Supercomputing at the Pittsburgh Supercomputing Center (P41 RR06009) and the National Center for Multiscale Modeling of Biological Systems (P41 GM103712); the Harvard Medical School Vision Core Grant (P30 EY12196); the Bertarelli Foundation; the Edward R. and Anne G. Lefler Center; the Stanley and Theodora Feldberg Fund; Neuro-Electronics Research Flanders (NERF); and the Allen Institute for Brain Science.
About the Allen Institute for Brain Science
The Allen Institute for Brain Science, a division of the Allen Institute (alleninstitute.org), is an independent, 501(c)(3) nonprofit medical research organization dedicated to accelerating the understanding of how the human brain works in health and disease. Using a big science approach, the Allen Institute generates useful public resources used by researchers and organizations around the globe, drives technological and analytical advances, and discovers fundamental brain properties through integration of experiments, modeling and theory. Launched in 2003 with a seed contribution from founder and philanthropist Paul G. Allen, the Allen Institute is supported by a diversity of government, foundation and private funds to enable its projects. Given the Institute’s achievements, Mr. Allen committed an additional $300 million in 2012 for the first four years of a ten-year plan to further propel and expand the Institute’s scientific programs, bringing his total commitment to date to $500 million. The Allen Institute’s data and tools are publicly available online at brain-map.org.
About Harvard Medical School
HMS has more than 7,500 full-time faculty working in 10 academic departments located at the School’s Boston campus or in hospital-based clinical departments at 15 Harvard-affiliated teaching hospitals and research institutes: Beth Israel Deaconess Medical Center, Boston Children’s Hospital, Brigham and Women’s Hospital, Cambridge Health Alliance, Dana-Farber Cancer Institute, Harvard Pilgrim Health Care Institute, Hebrew SeniorLife, Joslin Diabetes Center, Judge Baker Children’s Center, Massachusetts Eye and Ear/Schepens Eye Research Institute, Massachusetts General Hospital, McLean Hospital, Mount Auburn Hospital, Spaulding Rehabilitation Hospital and VA Boston Healthcare System.
About NERF
Neuro-Electronics Research Flanders (NERF; www.nerf.be) is a neurotechnology research initiative is headquartered in Leuven, Belgium initiated by imec, KU Leuven and VIB to unravel how electrical activity in the brain gives rise to mental function and behaviour. Imec performs world-leading research in nanoelectronics and has offices in Belgium, the Netherlands, Taiwan, USA, China, India and Japan. Its staff of about 2,200 people includes almost 700 industrial residents and guest researchers. In 2014, imec’s revenue (P&L) totaled 363 million euro. VIB is a life sciences research institute in Flanders, Belgium. With more than 1470 scientists from over 60 countries, VIB performs basic research into the molecular foundations of life. KU Leuven is one of the oldest and largest research universities in Europe with over 10,000 employees and 55,000 students.
ORIGINAL: Allen Institute
March 28th, 2016

Brain waves may be spread by weak electrical field

By Hugo Angel,

The research team says the electrical fields could be behind the spread of sleep and theta waves, along with epileptic seizure waves (Credit:Shutterstock)
Mechanism tied to waves associated with epilepsy
Researchers at Case Western Reserve University may have found a new way information is communicated throughout the brain.
Their discovery could lead to identifying possible new targets to investigate brain waves associated with memory and epilepsy and better understand healthy physiology.
They recorded neural spikes traveling at a speed too slow for known mechanisms to circulate throughout the brain. The only explanation, the scientists say, is the wave is spread by a mild electrical field they could detect. Computer modeling and in-vitro testing support their theory.
Others have been working on such phenomena for decades, but no one has ever made these connections,” said Steven J. Schiff, director of the Center for Neural Engineering at Penn State University, who was not involved in the study. “The implications are that such directed fields can be used to modulate both pathological activities, such as seizures, and to interact with cognitive rhythms that help regulate a variety of processes in the brain.
Scientists Dominique Durand, Elmer Lincoln Lindseth Professor in Biomedical Engineering at Case School of Engineering and leader of the research, former graduate student Chen Sui and current PhD students Rajat Shivacharan and Mingming Zhang, report their findings in The Journal of Neuroscience.
Researchers have thought that the brain’s endogenous electrical fields are too weak to propagate wave transmission,” Durand said. “But it appears the brain may be using the fields to communicate without synaptic transmissions, gap junctions or diffusion.
How the fields may work
Computer modeling and testing on mouse hippocampi (the central part of the brain associated with memory and spatial navigation) in the lab indicate the field begins in one cell or group of cells.
Although the electrical field is of low amplitude, the field excites and activates immediate neighbors, which, in turn, excite and activate immediate neighbors, and so on across the brain at a rate of about 0.1 meter per second.
Blocking the endogenous electrical field in the mouse hippocampus and increasing the distance between cells in the computer model and in-vitro both slowed the speed of the wave.
These results, the researchers say, confirm that the propagation mechanism for the activity is consistent with the electrical field.
Because sleep waves and theta waves–which are associated with forming memories during sleep–and epileptic seizure waves travel at about 1 meter per second, the researchers are now investigating whether the electrical fields play a role in normal physiology and in epilepsy.
If so, they will try to discern what information the fields may be carrying. Durand’s lab is also investigating where the endogenous spikes come from.
ORIGINAL: Eurkalert
14-JAN-2016

Memory capacity of brain is 10 times more than previously thought

By Hugo Angel,

Data from the Salk Institute shows brain’s memory capacity is in the petabyte range, as much as entire Web

LA JOLLA—Salk researchers and collaborators have achieved critical insight into the size of neural connections, putting the memory capacity of the brain far higher than common estimates. The new work also answers a longstanding question as to how the brain is so energy efficient and could help engineers build computers that are incredibly powerful but also conserve energy.
This is a real bombshell in the field of neuroscience,” said Terry Sejnowski from the Salk Institute for Biological Studies. “Our new measurements of the brain’s memory capacity increase conservative estimates by a factor of 10 to at least a petabyte (215 Bytes = 1000 TeraBytes), in the same ballpark as the World Wide Web.
Our memories and thoughts are the result of patterns of electrical and chemical activity in the brain. A key part of the activity happens when branches of neurons, much like electrical wire, interact at certain junctions, known as synapses. An output ‘wire’ (an axon) from one neuron connects to an input ‘wire’ (a dendrite) of a second neuron. Signals travel across the synapse as chemicals called neurotransmitters to tell the receiving neuron whether to convey an electrical signal to other neurons. Each neuron can have thousands of these synapses with thousands of other neurons.
When we first reconstructed every dendrite, axon, glial process, and synapse from a volume of hippocampus the size of a single red blood cell, we were somewhat bewildered by the complexity and diversity amongst the synapses,” says Kristen Harris, co-senior author of the work and professor of neuroscience at the University of Texas, Austin. “While I had hoped to learn fundamental principles about how the brain is organized from these detailed reconstructions, I have been truly amazed at the precision obtained in the analyses of this report.
Synapses are still a mystery, though their dysfunction can cause a range of neurological diseases. Larger synapses—with more surface area and vesicles of neurotransmitters—are stronger, making them more likely to activate their surrounding neurons than medium or small synapses.
The Salk team, while building a 3D reconstruction of rat hippocampus tissue (the memory center of the brain), noticed something unusual. In some cases, a single axon from one neuron formed two synapses reaching out to a single dendrite of a second neuron, signifying that the first neuron seemed to be sending a duplicate message to the receiving neuron.
At first, the researchers didn’t think much of this duplicity, which occurs about 10 percent of the time in the hippocampus. But Tom Bartol, a Salk staff scientist, had an idea: if they could measure the difference between two very similar synapses such as these, they might glean insight into synaptic sizes, which so far had only been classified in the field as small, medium and large.
In a computational reconstruction of brain tissue in the hippocampus, Salk scientists and UT-Austin scientists found the unusual occurrence of two synapses from the axon of one neuron (translucent black strip) forming onto two spines on the same dendrite of a second neuron (yellow). Separate terminals from one neuron’s axon are shown in synaptic contact with two spines (arrows) on the same dendrite of a second neuron in the hippocampus. The spine head volumes, synaptic contact areas (red), neck diameters (gray) and number of presynaptic vesicles (white spheres) of these two synapses are almost identical. Credit: Salk Institut
To do this, researchers used advanced microscopy and computational algorithms they had developed to image rat brains and reconstruct the connectivity, shapes, volumes and surface area of the brain tissue down to a nanomolecular level.
The scientists expected the synapses would be roughly similar in size, but were surprised to discover the synapses were nearly identical.
We were amazed to find that the difference in the sizes of the pairs of synapses were very small, on average, only about 8 percent different in size,” said Tom Bartol, one of the scientists. “No one thought it would be such a small difference. This was a curveball from nature.
Because the memory capacity of neurons is dependent upon synapse size, this eight percent difference turned out to be a key number the team could then plug into their algorithmic models of the brain to measure how much information could potentially be stored in synaptic connections.
It was known before that the range in sizes between the smallest and largest synapses was a factor of 60 and that most are small.
But armed with the knowledge that synapses of all sizes could vary in increments as little as eight percent between sizes within a factor of 60, the team determined there could be about 26 categories of sizes of synapses, rather than just a few.
Our data suggests there are 10 times more discrete sizes of synapses than previously thought,” says Bartol. In computer terms, 26 sizes of synapses correspond to about 4.7 “bits” of information. Previously, it was thought that the brain was capable of just one to two bits for short and long memory storage in the hippocampus.
This is roughly an order of magnitude of precision more than anyone has ever imagined,” said Sejnowski.
What makes this precision puzzling is that hippocampal synapses are notoriously unreliable. When a signal travels from one neuron to another, it typically activates that second neuron only 10 to 20 percent of the time.
We had often wondered how the remarkable precision of the brain can come out of such unreliable synapses,” says Bartol. One answer, it seems, is in the constant adjustment of synapses, averaging out their success and failure rates over time. The team used their new data and a statistical model to find out how many signals it would take a pair of synapses to get to that eight percent difference.
The researchers calculated that
  • for the smallest synapses, about 1,500 events cause a change in their size/ability (20 minutes) and
  • for the largest synapses, only a couple hundred signaling events (1 to 2 minutes) cause a change.
This means that every 2 or 20 minutes, your synapses are going up or down to the next size,” said Bartol. “The synapses are adjusting themselves according to the signals they receive.
From left: Terry Sejnowski, Cailey Bromer and Tom Bartol. Credit: Salk Institute
Our prior work had hinted at the possibility that spines and axons that synapse together would be similar in size, but the reality of the precision is truly remarkable and lays the foundation for whole new ways to think about brains and computers,” says Harris. “The work resulting from this collaboration has opened a new chapter in the search for learning and memory mechanisms.” Harris adds that the findings suggest more questions to explore, for example, if similar rules apply for synapses in other regions of the brain and how those rules differ during development and as synapses change during the initial stages of learning.
The implications of what we found are far-reaching. Hidden under the apparent chaos and messiness of the brain is an underlying precision to the size and shapes of synapses that was hidden from us.
The findings also offer a valuable explanation for the brain’s surprising efficiency. The waking adult brain generates only about 20 watts of continuous power—as much as a very dim light bulb. The Salk discovery could help computer scientists build ultra-precise but energy-efficient computers, particularly ones that employ deep learning and neural nets techniques capable of sophisticated learning and analysis, such as speech, object recognition and translation.
This trick of the brain absolutely points to a way to design better computers,”said Sejnowski. “Using probabilistic transmission turns out to be as accurate and require much less energy for both computers and brains.
Other authors on the paper were Cailey Bromer of the Salk Institute; Justin Kinney of the McGovern Institute for Brain Research; and Michael A. Chirillo and Jennifer N. Bourne of the University of Texas, Austin.
The work was supported by the NIH and the Howard Hughes Medical Institute.
ORIGINAL: Salk.edu
January 20, 2016

Forward to the Future: Visions of 2045

By Hugo Angel,

DARPA asked the world and our own researchers what technologies they expect to see 30 years from now—and received insightful, sometimes funny predictions
Today—October 21, 2015—is famous in popular culture as the date 30 years in the future when Marty McFly and Doc Brown arrive in their time-traveling DeLorean in the movie “Back to the Future Part II.” The film got some things right about 2015, including in-home videoconferencing and devices that recognize people by their voices and fingerprints. But it also predicted trunk-sized fusion reactors, hoverboards and flying cars—game-changing technologies that, despite the advances we’ve seen in so many fields over the past three decades, still exist only in our imaginations.
A big part of DARPA’s mission is to envision the future and make the impossible possible. So ten days ago, as the “Back to the Future” day approached, we turned to social media and asked the world to predict: What technologies might actually surround us 30 years from now? We pointed people to presentations from DARPA’s Future Technologies Forum, held last month in St. Louis, for inspiration and a reality check before submitting their predictions.
Well, you rose to the challenge and the results are in. So in honor of Marty and Doc (little known fact: he is a DARPA alum) and all of the world’s innovators past and future, we present here some highlights from your responses, in roughly descending order by number of mentions for each class of futuristic capability:
  • Space: Interplanetary and interstellar travel, including faster-than-light travel; missions and permanent settlements on the Moon, Mars and the asteroid belt; space elevators
  • Transportation & Energy: Self-driving and electric vehicles; improved mass transit systems and intercontinental travel; flying cars and hoverboards; high-efficiency solar and other sustainable energy sources
  • Medicine & Health: Neurological devices for memory augmentation, storage and transfer, and perhaps to read people’s thoughts; life extension, including virtual immortality via uploading brains into computers; artificial cells and organs; “Star Trek”-style tricorder for home diagnostics and treatment; wearable technology, such as exoskeletons and augmented-reality glasses and contact lenses
  • Materials & Robotics: Ubiquitous nanotechnology, 3-D printing and robotics; invisibility and cloaking devices; energy shields; anti-gravity devices
  • Cyber & Big Data: Improved artificial intelligence; optical and quantum computing; faster, more secure Internet; better use of data analytics to improve use of resources
A few predictions inspired us to respond directly:
  • Pizza delivery via teleportation”—DARPA took a close look at this a few years ago and decided there is plenty of incentive for the private sector to handle this challenge.
  • Time travel technology will be close, but will be closely guarded by the military as a matter of national security”—We already did this tomorrow.
  • Systems for controlling the weather”—Meteorologists told us it would be a job killer and we didn’t want to rain on their parade.
  • Space colonies…and unlimited cellular data plans that won’t be slowed by your carrier when you go over a limit”—We appreciate the idea that these are equally difficult, but they are not. We think likable cell-phone data plans are beyond even DARPA and a total non-starter.
So seriously, as an adjunct to this crowd-sourced view of the future, we asked three DARPA researchers from various fields to share their visions of 2045, and why getting there will require a group effort with players not only from academia and industry but from forward-looking government laboratories and agencies:

Pam Melroy, an aerospace engineer, former astronaut and current deputy director of DARPA’s Tactical Technologies Office (TTO), foresees technologies that would enable machines to collaborate with humans as partners on tasks far more complex than those we can tackle today:
Justin Sanchez, a neuroscientist and program manager in DARPA’s Biological Technologies Office (BTO), imagines a world where neurotechnologies could enable users to interact with their environment and other people by thought alone:
Stefanie Tompkins, a geologist and director of DARPA’s Defense Sciences Office, envisions building substances from the atomic or molecular level up to create “impossible” materials with previously unattainable capabilities.
Check back with us in 2045—or sooner, if that time machine stuff works out—for an assessment of how things really turned out in 30 years.
# # #
Associated images posted on www.darpa.mil and video posted at www.youtube.com/darpatv may be reused according to the terms of the DARPA User Agreement, available here:http://www.darpa.mil/policy/usage-policy.
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ORIGINAL: DARPA
10/21/2015

Scaling up synthetic-biology innovation

By Hugo Angel,

.
Gen9’s BioFab platform synthesizes small DNA fragments on silicon chips
and uses other technologies to build longer DNA constructs from those
fragments. Done in a parallel, this produces hundreds to thousands of
DNA constructs simultaneously. Shown here is an automated
liquid-handling instrument that dispenses DNA onto the chips. Courtesy of Gen9
MIT professor’s startup makes synthesizing genes many times more cost effective.
Inside and outside of the classroom, MIT professor Joseph Jacobson has become a prominent figure in — and advocate for — the emerging field of synthetic biology.

As head of the Molecular Machines group at the MIT Media Lab, Jacobson’s work has focused on, among other things, developing technologies for the rapid fabrication of DNA molecules. In 2009, he spun out some of his work into .Gen9, which aims to boost synthetic-biology innovation by offering scientists more cost-effective tools and resources.
Headquartered in Cambridge, Massachusetts, Gen9 has developed a method for synthesizing DNA on silicon chips, which significantly cuts costs and accelerates the creation and testing of genes. Commercially available since 2013, the platform is now being used by dozens of scientists and commercial firms worldwide.
Synthetic biologists synthesize genes by combining strands of DNA. These new genes can be inserted into microorganisms such as yeast and bacteria. Using this approach, scientists can tinker with the cells’ metabolic pathways, enabling the microbes to perform new functions, including testing new antibodies, sensing chemicals in an environment, or creating biofuels.

But conventional gene-synthesizing methods can be time-consuming and costly. Chemical-based processes, for instance, cost roughly 20 cents per base pair — DNA’s key building block — and produce one strand of DNA at a time. This adds up in time and money when synthesizing genes comprising 100,000 base pairs.

Gen9’s chip-based DNA, however, drops the price to roughly 2 cents per base pair, Jacobson says. Additionally, hundreds of thousands of base pairs can be tested and compiled in parallel, as opposed to testing and compiling each pair individually through conventional methods.

This means faster testing and development of new pathways — which usually takes many years — for applications such as advanced therapeutics, and more effective enzymes for detergents, food processing, and biofuels, Jacobson says. “If you can build thousands of pathways on a chip in parallel, and can test them all at once, you get to a working metabolic pathway much faster,” he says.

Over the years, Jacobson and Gen9 have earned many awards and honors. In November, Jacobson was also inducted into the National Inventors Hall of Fame for co-inventing E Ink, the electronic ink used for Amazon’s Kindle e-reader display.

Scaling gene synthesizing Throughout the early-and mid-2000s, a few important pieces of research came together to allow for the scaling up of gene synthesis, which ultimately led to Gen9.

First, Jacobson and his students Chris Emig and Brian Chow began developing chips with thousands of “spots,” which each contained about 100 million copies of a different DNA sequence.

Then, Jacobson and another student, David Kong, created a process that used a certain enzyme as a catalyst to assemble those small DNA fragments into larger DNA strands inside microfluidics devices — “which was the first microfluidics assembly of DNA ever,” Jacobson says.

Despite the novelty, however, the process still wasn’t entirely cost effective. On average, it produced a 99 percent yield, meaning that about 1 percent of the base pairs didn’t match when constructing larger strands. That’s not so bad for making genes with 100 base pairs. “But if you want to make something that’s 10,000 or 100,000 bases long, that’s no good anymore,” Jacobson says.

Around 2004, Jacobson and then-postdoc Peter Carr, along with several other students, found a way to drastically increase yields by taking a cue from a natural error-correcting protein, Mut-S, which recognizes mismatches in DNA base pairing that occur when two DNA strands form a double helix. For synthetic DNA, the protein can detect and extract mismatches arising in base pairs synthesized on the chip, improving yields. In a paper published that year in Nucleic Acids Research, the researchers wrote that this process reduces the frequency of errors, from one in every 100 base pairs to around one in every 10,000.

With these innovations, Jacobson launched Gen9 with two co-founders: George Church of Harvard University, who was also working on synthesizing DNA on microchips, and Drew Endy of Stanford University, a world leader in synthetic-biology innovations.

Together with employees, they created a platform called BioFab and several other tools for synthetic biologists. Today, clients use an online portal to order gene sequences. Then Gen9 designs and fabricates those sequences on chips and delivers them to customers. Recently, the startup updated the portal to allow drag-and-drop capabilities and options for editing and storing gene sequences.

This allows users to “make these very extensive libraries that have been inaccessible previously,” Jacobson says.


Fueling big ideas

Many published studies have already used Gen9’s tools, several of which are posted to the startup’s website. Notable ones, Jacobson says, include designing proteins for therapeutics. In those cases, the researcher needs to make 10 million or 100 million versions of a protein, each comprising maybe 50,000 pieces of DNA, to see which ones work best.

Instead of making and testing DNA sequences one at a time with conventional methods, Gen9 lets researchers test hundreds of thousands of sequences at once on a chip. This should increase chances of finding the right protein, more quickly. “If you just have one shot you’re very unlikely to hit the target,” Jacobson says. “If you have thousands or tens of thousands of shots on a goal, you have a much better chance of success.


Currently, all the world’s synthetic-biology methods produce only about 300 million bases per year. About 10 of the chips Gen9 uses to make DNA can hold the same amount of content, Jacobson says. In principle, he says, the platform used to make Gen9’s chips — based on collaboration with manufacturing firm Agilent — could produce enough chips to cover about 200 billion bases. This is about the equivalent capacity of GenBank, an open-access database of DNA bases and gene sequences that has been constantly updated since the 1980s.

Such technology could soon be worth a pretty penny: According to a study published in November by MarketsandMarkets, a major marketing research firm, the market for synthesizing short DNA strands is expected to reach roughly $1.9 billion by 2020.

Still, Gen9 is pushing to drop costs for synthesis to under 1 cent per base pair, Jacobson says. Additionally, for the past few years, the startup has hosted an annual G-Prize Competition, which awards 1 million base pairs of DNA to researchers with creative synthetic-biology ideas. That’s a prize worth roughly $100,000.

The aim, Jacobson says, is to remove cost barriers for synthetic biologists to boost innovation. “People have lots of ideas but are unable to try out those ideas because of cost,” he says. “This encourages people to think about bigger and bigger ideas.”

ORIGINAL: .MIT News

Rob Matheson | MIT News Office
December 10, 2015

DNA Is Multibillion-Year-Old Software

By Hugo Angel,

Illustration by Julia Suits, The New Yorker Cartoonist & author of The Extraordinary Catalog of Peculiar Inventions.
Illustration by Julia Suits, The New Yorker Cartoonist & author of The Extraordinary Catalog of Peculiar Inventions.
Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin. Life requires what software does (it’s foundationally algorithmic).

1. “DNA is multibillion-year-old software,says Chaitin (inventor of mathematical metabiology). We’re surrounded by software, but couldn’t see it until we had suitable thinking tools.
2. Alan Turing described modern software in 1936, inspiring John Von Neumann to connect software to biology. Before DNA was understood, Von Neumann saw that self-reproducing automata needed software. We now know DNA stores information; it’s a biochemical version of Turning’s software tape, but more generally: All that lives must process information. Biology’s basic building blocks are processes that make decisions.
3. Casting life as software provides many technomorphic insights (and mis-analogies), but let’s consider just its informational complexity. Do life’s patterns fit the tools of simpler sciences, like physics? How useful are experiments? Algebra? Statistics?
4. The logic of life is more complex than the inanimate sciences need. The deep structure of life’s interactions are algorithmic (loosely algorithms = logic with if-then-else controls). Can physics-friendly algebra capture life’s biochemical computations?
5. Describing its “pernicious influence” on science, Jack Schwartz says, mathematics succeeds in only “the simplest of situations” or when “rare good fortune makes [a] complex situation hinge upon a few dominant simple factors.”
6. Physics has low “causal density” — a great Jim Manzi coinage. Nothing in physics chooses. Or changes how it chooses. A few simple factors dominate, operating on properties that generally combine in simple ways. Its parameters are independent. Its algebra-friendly patterns generalize well (its equations suit stable categories and equilibrium states).
7. Higher-causal-density domains mean harder experiments (many hard-to-control factors that often can’t be varied independently). Fields like medicine can partly counter their complexity by randomized trials, but reliable generalization requires biological “uniformity of response.”
8. Social sciences have even higher causal densities, so “generalizing from even properly randomized experiments” is “hazardous,” Manzi says. “Omitted variable bias” in human systems is “massive.” Randomization ≠ representativeness of results is guaranteed. 
9. Complexity economist Brian Arthur says science’s pattern-grasping toolbox is becoming “more algorithmic … and less equation-based. But the nascent algorithmic era hasn’t had its Newton yet.
10. With studies in high-causal-density fields, always consider how representative data is, and ponder if uniform or stable responses are plausible. Human systems are often highly variable; our behaviors aren’t homogenous; they can change types; they’re often not in equilibrium.
11. Bad examples: Malcolm Gladwell puts entertainment first (again) by asserting that “the easiest way to raise people’s scores” is to make a test less readable (n = 40 study, later debunked). Also succumbing to unwarranted extrapolation, leading data-explainer Ezra Klein said, “Cutting-edge research shows that the more information partisans get, the deeper their disagreements.” That study neither represents all kinds of information, nor is a uniform response likely (in fact, assuming that would be ridiculous). Such rash generalizations = far from spotless record. 
Mismatched causal density and thinking tools creates errors. Entire fields are built on assuming such (mismatched) metaphors and methods
Related
olicausal sciences; Newton pattern vs. Darwin pattern; the two kinds of data (history ≠ nomothetic); life = game theoretic = fundamentally algorithmic.
(Hat tip to Bryan Atkins @postgenetic for pointer to Brian Arthur).
ORIGINAL: Big Think
5 MONTHS AGO

How Your Brain Is Wired Reveals the Real You

By Hugo Angel,

The Human Connectome Project finds surprising correlations between brain architecture and behavior
©iStock.com
The brain’s wiring patterns can shed light on a person’s positive and negative traits, researchers report in Nature Neuroscience. The finding, published on September 28, is the first from the Human Connectome Project (HCP), an international effort to map active connections between neurons in different parts of the brain.
The HCP, which launched in 2010 at a cost of US$40 million, seeks to scan the brain networks, or connectomes, of 1,200 adults. Among its goals is to chart the networks that are active when the brain is idle; these are thought to keep the different parts of the brain connected in case they need to perform a task.
In April, a branch of the project led by one of the HCP’s co-chairs, biomedical engineer Stephen Smith at the University of Oxford, UK, released a database of resting-state connectomes from about 460 people between 22 and 35 years old. Each brain scan is supplemented by information on approximately 280 traits, such as the person’s age, whether they have a history of drug use, their socioeconomic status and personality traits, and their performance on various intelligence tests.
Axis of connectivity
Smith and his colleagues ran a massive computer analysis to look at how these traits varied among the volunteers, and how the traits correlated with different brain connectivity patterns. The team was surprised to find a single, stark difference in the way brains were connected. People with more ‘positive’ variables, such as more education, better physical endurance and above-average performance on memory tests, shared the same patterns. Their brains seemed to be more strongly connected than those of people with ‘negative’ traits such as smoking, aggressive behaviour or a family history of alcohol abuse.
Marcus Raichle, a neuroscientist at Washington University in St Louis, Missouri, is impressed that the activity and anatomy of the brains alone were enough to reveal this ‘positive-negative’ axis. “You can distinguish people with successful traits and successful lives versus those who are not so successful,” he says.
But Raichle says that it is impossible to determine from this study how different traits relate to one another and whether the weakened brain connections are the cause or effect of negative traits. And although the patterns are clear across the large group of HCP volunteers, it might be some time before these connectivity patterns could be used to predict risks and traits in a given individual. Deanna Barch, a psychologist at Washington University who co-authored the latest study, says that once these causal relationships are better understood, it might be possible to push brains toward the ‘good’ end of the axis.
Van Wedeen, a neuroscientist at Massachusetts General Hospital in Boston, says that the findings could help to prioritize future research. For instance, one of the negative traits that pulled a brain farthest down the negative axis was marijuana use in recent weeks. Wedeen says that the finding emphasizes the importance of projects such as one launched by the US National Institute on Drug Abuse last week, which will follow 10,000 adolescents for 10 years to determine how marijuana and other drugs affect their brains.
Wedeen finds it interesting that the wiring patterns associated with people’s general intelligence scores were not exactly the same as the patterns for individual measures of cognition—people with good hand–eye coordination, for instance, fell farther down the negative axis than did those with good verbal memory. This suggests that the biology underlying cognition might be more complex than our current definition of general intelligence, and that it could be influenced by demographic and behavioural factors. “Maybe it will cause us to reconsider what [the test for general intelligence] is measuring,” he says. “We have a new mystery now.
Much more connectome data should emerge in the next few years. The Harvard Aging Brain Study, for instance, is measuring active brain connections in 284 people aged between 65 and 90, and released its first data earlier this year. And Smith is running the Developing Human Connectome Project in the United Kingdom, which is imaging the brains of 1,200 babies before and after birth. He expects to release its first data in the next few months. Meanwhile, the HCP is analysing genetic data from its participants, which include a large number of identical and fraternal twins, to determine how genetic and environmental factors relate to brain connectivity patterns.
This article is reproduced with permission and was first published on September 28, 2015.
September 28, 2015

First almost fully-formed human brain grown in lab, researchers claim

By admin,

Research team say tiny brain could be used to test drugs and study diseases, but scientific peers urge caution as data on breakthrough kept under wraps
The tiny brain, which resembles that of a five-week-old foetus, is not conscious. Photograph: Ohio State University
An almost fully-formed human brain has been grown in a lab for the first time, claim scientists from Ohio State University. The team behind the feat hope the brain could transform our understanding of neurological disease.
Though not conscious the miniature brain, which resembles that of a five-week-old foetus, could potentially be useful for scientists who want to study the progression of developmental diseases. It could also be used to test drugs for conditions such as Alzheimer’s and Parkinson’s, since the regions they affect are in place during an early stage of brain development.
The brain, which is about the size of a pencil eraser, is engineered from adult human skin cells and is the most complete human brain model yet developed, claimed Rene Anand of Ohio State University, Columbus, who presented the work today at the Military Health System Research Symposium in Fort Lauderdale, Florida.
Scientists create lab-grown spinal cords
Previous attempts at growing whole brains have at best achieved mini-organs that resemble those of nine-week-old foetuses, although these “cerebral organoids” were not complete and only contained certain aspects of the brain. “We have grown the entire brain from the get-go,” said Anand.
Anand and his colleagues claim to have reproduced 99% of the brain’s diverse cell types and genes. They say their brain also contains a spinal cord, signalling circuitry and even a retina.
The ethical concerns were non-existent, said Anand. “We don’t have any sensory stimuli entering the brain. This brain is not thinking in any way.”
Anand claims to have created the brain by converting adult skin cells into pluripotent cells: stem cells that can be programmed to become any tissue in the body. These were then grown in a specialised environment that persuaded the stem cells to grow into all the different components of the brain and central nervous system.
According to Anand, it takes about 12 weeks to create a brain that resembles the maturity of a five-week-old foetus. To go further would require a network of blood vessels that the team cannot yet produce. “We’d need an artificial heart to help the brain grow further in development,” said Anand.
Several researchers contacted by the Guardian said it was hard to judge the quality of the work without access to more data, which Anand is keeping under wraps due to a pending patent on the technique. Many were uncomfortable that the team had released information to the press without the science having gone through peer review.
Zameel Cader, a consultant neurologist at the John Radcliffe Hospital, Oxford, said that while the work sounds very exciting, it’s not yet possible to judge its impact. “When someone makes such an extraordinary claim as this, you have to be cautious until they are willing to reveal their data.
3D-printed brain tissue
If the team’s claims prove true, the technique could revolutionise personalised medicine. “If you have an inherited disease, for example, you could give us a sample of skin cells, we could make a brain and then ask what’s going on,” said Anand.
You could also test the effect of different environmental toxins on the growing brain, he added. “We can look at the expression of every gene in the human genome at every step of the development process and see how they change with different toxins. Maybe then we’ll be able to say ‘holy cow, this one isn’t good for you.’
For now, the team say they are focusing on using the brain for military research, to understand the effect of post traumatic stress disorder and traumatic brain injuries.
ORIGINAL: The Guardian
Tuesday 18 August 2015

Peeking into the brain’s filing system

By admin,

Aspects of a single memory can be scattered throughout the outer “cortex” of the brain
Storing information so that you can easily find it again is a challenge. From purposefully messy desks to indexed filing cabinets, we all have our preferred systems. How does it happen inside our brains?
Somewhere within the dense, damp and intricate 1.5kg of tissue that we carry in our skulls, all of our experiences are processed, stored, and – sometimes more readily than others – retrieved again when we need them.
It’s what neuroscientists call “episodic memory” and for years, they have loosely agreed on a model for how it works. Gathering detailed data to flesh out that model is difficult.
But the picture is beginning to get clearer and more complete.
A key component is the small, looping structure called the hippocampus, buried quite deep beneath the brain’s wrinkled outer layer. It is only a few centimetres in length but is very well connected to other parts of the brain.
People with damage to their hippocampus have profound memory problems and this has made it a major focus of memory research since the 1950s.
Quick learning
It was in the hippocampus, and some of its neighbouring brain regions, that scientists from the University of Leicester got a glimpse of new memories being formed, in a study published this week.
Single brain cells in the hippocampus can form associations very rapidly
They used a rare opportunity to record the fizz and crackle of single human brain cells at work, in epilepsy patients undergoing brain surgery.
Individual neurons that went crazy for particular celebrities, like Clint Eastwood, could be “trained” to respond to, for example, the Statue of Liberty as well – as soon as the patients were given a picture of Clint in front of the statue.
It seemed that single brain cells, in the hippocampus, had been caught in the act of forming a new association. And they do it very fast.
But that outer wrapping of the brain – the cortex – is also important. It is much bigger than the hippocampus and does myriad jobs, from sensing the world to moving our limbs.
When we have a particular experience, like a trip to the beach, different patches of the cortex are called up to help us process different elements: recognising a friend, hearing the seagulls, feeling the breeze.
So traces of that experience are rather scattered across the cortex.To remember it, the brain needs some sort of index to find them all again.
And that, neuroscientists generally agree, is where the hippocampus comes in.
Think of the [cortex] as a huge library and the hippocampus as its librarian,” wrote the prominent Hungarian neuroscientist Gyorgy Buszaki in his 2006 book Rhythms of the Brain.

Does the brain have a librarian?
The elements of our day at the beach might litter the cortex like specific books along miles of shelving; the hippocampus is able to link them together and – if all goes well – pull them off the shelf when we want to reminisce.
Completing patterns
Another brand new study, out this week in the journal Nature Communications, looks inside the brain using fMRI imaging to see this filing system in action.
By getting people to learn and remember imaginary scenarios while inside a brain scanner, Dr Aidan Horner and his colleagues at University College London collected the first firm evidence for “pattern completion” in the human hippocampus.
Pattern completion is the mechanism behind a phenomenon we all recognise, when one particular aspect of a memory – the smell of salt in the air, perhaps – brings all the other aspects flooding back.
If you have an event that involves the Eiffel tower, your friend and, say, a pink balloon… I can show you a picture of the Eiffel tower, and you remember not only your friend, but also the pink balloon,” Dr Horner told the BBC.
While his volunteers had just this sort of experience inside the scanner, Dr Horner saw interplay between different parts of the cortex, associated with different parts of a memory, and the hippocampus.
The brain activity flowed in a way that showed “pattern completion” was indeed underway – and the cortex and the hippocampus were working just like the library and the librarian in Prof Buzsaki’s analogy.

The hippocampus (darker brown) is centrally located and very well connected
If I cue you with the location, and I get you to explicitly retrieve the person, what we also see is activation in the region that’s associated with the object for that event,” Dr Horner explained. “So even though it’s task-irrelevant, you don’t have to retrieve it, it seems that we still bring that object to mind.
And the extent to which we see that activation in the ‘object’ region correlates with the hippocampal response. So that suggests that it’s the hippocampus that’s doing the pattern completion, retrieving all these elements.
It’s able to act as an index, I suppose, by linking these things together – and doing it very very quickly, that’s the key thing.
If the cortex were left to make its own connections between the fragments of a memory, he added, it would be far too slow.
That’s clearly not a system we want, if we’re going to remember a specific event that happens once in a lifetime.
Beat this: Episodic memory is a key challenge for artificial intelligence systems
Dr Horner said the findings also dovetail nicely with the single-neuron, celebrity-spotting results from the Leicester study.
We can look across the cortex and the hippocampus, and we can relate it to recollection. But what they can do is say look, these cells [in the hippocampus] have learned really quickly.
So that’s the sort of underlying neural basis of what we’re looking at, at a slightly broader scale.
Science, it seems, is finally managing to unpick the way our brains record our lives. It is a remarkable, beautiful, fallible system.
Building some sort of memory storage like this is regarded as one of the next key challenges for researchers trying to build intelligent machines.
Our own memories, for all their flaws, are a hard act to follow.
ORIGINAL: BBC
By Jonathan WebbScience reporter, BBC News
5 July 2015

Scientists have built artificial neurons that fully mimic human brain cells

By admin,

They could supplement our brain function.

Researchers have built the world’s first artificial neuron that’s capable of mimicking the function of an organic brain cell – including the ability to translate chemical signals into electrical impulses, and communicate with other human cells.
These artificial neurons are the size of a fingertip and contain no ‘living’ parts, but the team is working on shrinking them down so they can be implanted into humans. This could allow us to effectively replace damaged nerve cells and develop new treatments for neurological disorders, such as spinal cord injuries and Parkinson’s disease.
Our artificial neuron is made of conductive polymers and it functions like a human neuron,” lead researcher Agneta Richter-Dahlfors from the Karolinska Institutet in Sweden said in a press release.

Agneta Richter-Dahlfors

Until now, scientists have only been able to stimulate brain cells using electrical impulses, which is how they transmit information within the cells. But in our bodies they’re stimulated by chemical signals, and this is how they communicate with other neurons.
By connecting enzyme-based biosensors to organic electronic ion pumps, Richter-Dahlfors and her team have now managed to create an artificial neuron that can mimic this function, and they’ve shown that it can communicate chemically with organic brain cells even over large distances.
The sensing component of the artificial neuron senses a change in chemical signals in one dish, and translates this into an electrical signal,said Richter-Dahlfors. “This electrical signal is next translated into the release of the neurotransmitter acetylcholine in a second dish, whose effect on living human cells can be monitored.
This means that artificial neurons could theoretically be integrated into complex biological systems, such as our bodies, and could allow scientists to replace or bypass damaged nerve cells. So imagine being able to use the device to restore function to paralysed patients, or heal brain damage.
Next, we would like to miniaturise this device to enable implantation into the human body,said Richer-Dahlfors.“We foresee that in the future, by adding the concept of wireless communication, the biosensor could be placed in one part of the body, and trigger release of neurotransmitters at distant locations.
Using such auto-regulated sensing and delivery, or possibly a remote control, new and exciting opportunities for future research and treatment of neurological disorders can be envisaged,she added.
The results of lab trials have been published in the journal Biosensors and Bioelectronics.
We’re really looking forward to seeing where this research goes. While the potential for treating neurological disorders are incredibly exciting, the artificial neurons could one day also help us to supplement our mental abilities and add extra memory storage or offer faster processing, and that opens up some pretty awesome possibilities.
ORIGINAL: Science Alert
By FIONA MACDONALD
29 JUN 2015