Category: Neuroscience


Google DeepMind has built an AI machine that could learn as quickly as humans before long

By Hugo Angel,

Neural Episodic Control. Architecture of episodic memory module for a single action

Emerging Technology from the arXiv

Intelligent machines have humans in their sights.

Deep-learning machines already have superhuman skills when it comes to tasks such as

  • face recognition,
  • video-game playing, and
  • even the ancient Chinese game of Go.

So it’s easy to think that humans are already outgunned.

But not so fast. Intelligent machines still lag behind humans in one crucial area of performance: the speed at which they learn. When it comes to mastering classic video games, for example, the best deep-learning machines take some 200 hours of play to reach the same skill levels that humans achieve in just two hours.

So computer scientists would dearly love to have some way to speed up the rate at which machines learn.

Today, Alexander Pritzel and pals at Google’s DeepMind subsidiary in London claim to have done just that. These guys have built a deep-learning machine that is capable of rapidly assimilating new experiences and then acting on them. The result is a machine that learns significantly faster than others and has the potential to match humans in the not too distant future.

First, some background.

Deep learning uses layers of neural networks to look for patterns in data. When a single layer spots a pattern it recognizes, it sends this information to the next layer, which looks for patterns in this signal, and so on.

So in face recognition,

  • one layer might look for edges in an image,
  • the next layer for circular patterns of edges (the kind that eyes and mouths make), and
  • the next for triangular patterns such as those made by two eyes and a mouth.
  • When all this happens, the final output is an indication that a face has been spotted.

Of course, the devil is in the details. There are various systems of feedback to allow the system to learn by adjusting various internal parameters such as the strength of connections between layers. These parameters must change slowly, since a big change in one layer can catastrophically affect learning in the subsequent layers. That’s why deep neural networks need so much training and why it takes so long.

Pritzel and co have tackled this problem with a technique they call Neural Episodic Control. “Neural episodic control demonstrates dramatic improvements on the speed of learning for a wide range of environments,” they say. “Critically, our agent is able to rapidly latch onto highly successful strategies as soon as they are experienced, instead of waiting for many steps of optimisation.

The basic idea behind DeepMind’s approach is to copy the way humans and animals learn quickly. The general consensus is that humans can tackle situations in two different ways.

  • If the situation is familiar, our brains have already formed a model of it, which they use to work out how best to behave. This uses a part of the brain called the prefrontal cortex.
  • But when the situation is not familiar, our brains have to fall back on another strategy. This is thought to involve a much simpler test-and-remember approach involving the hippocampus. So we try something and remember the outcome of this episode. If it is successful, we try it again, and so on. But if it is not a successful episode, we try to avoid it in future.

This episodic approach suffices in the short term while our prefrontal brain learns. But it is soon outperformed by the prefrontal cortex and its model-based approach.

Pritzel and co have used this approach as their inspiration. Their new system has two approaches.

  • The first is a conventional deep-learning system that mimics the behaviur of the prefrontal cortex.
  • The second is more like the hippocampus. When the system tries something new, it remembers the outcome.

But crucially, it doesn’t try to learn what to remember. Instead, it remembers everything. “Our architecture does not try to learn when to write to memory, as this can be slow to learn and take a significant amount of time,” say Pritzel and co. “Instead, we elect to write all experiences to the memory, and allow it to grow very large compared to existing memory architectures.

They then use a set of strategies to read from this large memory quickly. The result is that the system can latch onto successful strategies much more quickly than conventional deep-learning systems.

They go on to demonstrate how well all this works by training their machine to play classic Atari video games, such as Breakout, Pong, and Space Invaders. (This is a playground that DeepMind has used to train many deep-learning machines.)

The team, which includes DeepMind cofounder Demis Hassibis, shows that neural episodic control vastly outperforms other deep-learning approaches in the speed at which it learns. “Our experiments show that neural episodic control requires an order of magnitude fewer interactions with the environment,” they say.

That’s impressive work with significant potential. The researchers say that an obvious extension of this work is to test their new approach on more complex 3-D environments.

It’ll be interesting to see what environments the team chooses and the impact this will have on the real world. We’ll look forward to seeing how that works out.

Ref: Neural Episodic Control : arxiv.org/abs/1703.01988

ORIGINAL: MIT Technology Review

A Giant Neuron Has Been Found Wrapped Around the Entire Circumference of the Brain

By Hugo Angel,

Allen Institute for Brain Science

This could be where consciousness forms. For the first time, scientists have detected a giant neuron wrapped around the entire circumference of a mouse’s brain, and it’s so densely connected across both hemispheres, it could finally explain the origins of consciousness.

Using a new imaging technique, the team detected the giant neuron emanating from one of the best-connected regions in the brain, and say it could be coordinating signals from different areas to create conscious thought.

This recently discovered neuron is one of three that have been detected for the first time in a mammal’s brain, and the new imaging technique could help us figure out if similar structures have gone undetected in our own brains for centuries.

At a recent meeting of the Brain Research through Advancing Innovative Neurotechnologies initiative in Maryland, a team from the Allen Institute for Brain Science described how all three neurons stretch across both hemispheres of the brain, but the largest one wraps around the organ’s circumference like a “crown of thorns”.
You can see them highlighted in the image at the top of the page.

Lead researcher Christof Koch told Sara Reardon at Nature that they’ve never seen neurons extend so far across both regions of the brain before.
Oddly enough, all three giant neurons happen to emanate from a part of the brain that’s shown intriguing connections to human consciousness in the past – the claustrum, a thin sheet of grey matter that could be the most connected structure in the entire brain, based on volume.

This relatively small region is hidden between the inner surface of the neocortex in the centre of the brain, and communicates with almost all regions of cortex to achieve many higher cognitive functions such as

  • language,
  • long-term planning, and
  • advanced sensory tasks such as
  • seeing and
  • hearing.

Advanced brain-imaging techniques that look at the white matter fibres coursing to and from the claustrum reveal that it is a neural Grand Central Station,Koch wrote for Scientific American back in 2014. “Almost every region of the cortex sends fibres to the claustrum.”

The claustrum is so densely connected to several crucial areas in the brain that Francis Crick of DNA double helix fame referred to it a “conductor of consciousnessin a 2005 paper co-written with Koch.

They suggested that it connects all of our external and internal perceptions together into a single unifying experience, like a conductor synchronises an orchestra, and strange medical cases in the past few years have only made their case stronger.

Back in 2014, a 54-year-old woman checked into the George Washington University Medical Faculty Associates in Washington, DC, for epilepsy treatment.

This involved gently probing various regions of her brain with electrodes to narrow down the potential source of her epileptic seizures, but when the team started stimulating the woman’s claustrum, they found they could effectively ‘switch’ her consciousness off and on again.

Helen Thomson reported for New Scientist at the time:
When the team zapped the area with high frequency electrical impulses, the woman lost consciousness. She stopped reading and stared blankly into space, she didn’t respond to auditory or visual commands and her breathing slowed.

As soon as the stimulation stopped, she immediately regained consciousness with no memory of the event. The same thing happened every time the area was stimulated during two days of experiments.”

According to Koch, who was not involved in the study, this kind of abrupt and specific ‘stopping and starting‘ of consciousness had never been seen before.

Another experiment in 2015 examined the effects of claustrum lesions on the consciousness of 171 combat veterans with traumatic brain injuries.

They found that claustrum damage was associated with the duration, but not frequency, of loss of consciousness, suggesting that it could play an important role in the switching on and off of conscious thought, but another region could be involved in maintaining it.

And now Koch and his team have discovered extensive neurons in mouse brains emanating from this mysterious region.

In order to map neurons, researchers usually have to inject individual nerve cells with a dye, cut the brain into thin sections, and then trace the neuron’s path by hand.

It’s a surprisingly rudimentary technique for a neuroscientist to have to perform, and given that they have to destroy the brain in the process, it’s not one that can be done regularly on human organs.

Koch and his team wanted to come up with a technique that was less invasive, and engineered mice that could have specific genes in their claustrum neurons activated by a specific drug.

When the researchers fed the mice a small amount of the drug, only a handful of neurons received enough of it to switch on these genes,Reardon reports for Nature.

That resulted in production of a green fluorescent protein that spread throughout the entire neuron. The team then took 10,000 cross-sectional images of the mouse brain, and used a computer program to create a 3D reconstruction of just three glowing cells.

We should keep in mind that just because these new giant neurons are connected to the claustrum doesn’t mean that Koch’s hypothesis about consciousness is correct – we’re a long way from proving that yet.

It’s also important to note that these neurons have only been detected in mice so far, and the research has yet to be published in a peer-reviewed journal, so we need to wait for further confirmation before we can really delve into what this discovery could mean for humans.

But the discovery is an intriguing piece of the puzzle that could help up make sense of this crucial, but enigmatic region of the brain, and how it could relate to the human experience of conscious thought.

The research was presented at the 15 February meeting of the Brain Research through Advancing Innovative Neurotechnologies initiative in Bethesda, Maryland.

ORIGINAL: ScienceAlert

BEC CREW
28 FEB 2017

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

A lab founded by a tech billionaire just unveiled a major leap forward in cracking your brain’s code

By Hugo Angel,

This is definitely not a scene from “A Clockwork Orange.” Allen Brain Observatory
As the mice watched a computer screen, their glowing neurons pulsed through glass windows in their skulls.
Using a device called a two-photon microscope, researchers at the Allen Institute for Brain Science could peer through those windows and record, layer by layer, the workings of their little minds.
The result, announced July 13, is a real-time record of the visual cortex — a brain region shared in similar form across mammalian species — at work. The data set that emerged is so massive and complete that its creators have named it the Allen Brain Observatory.
Bred for the lab, the mice were genetically modified so that specific cells in their brains would fluoresce when they became active. Researchers had installed the brain-windows surgically, slicing away tiny chunks of the rodents’ skulls and replacing them with five-millimeter skylights.
Sparkling neurons of the mouse visual cortex shone through the glass as images and short films flashed across the screen. Each point of light the researchers saw translated, with hours of careful processing, into data: 
  • Which cell lit up? 
  • Where in the brain? 
  • How long did it glow? 
  • What was the mouse doing at the time? 
  • What was on the screen?

The researchers imaged the neurons in small groups, building a map of one microscopic layer before moving down to the next. When they were finished, the activities of 18,000 cells from several dozen mice were recorded in their database.

This is the first data set where we’re watching large populations of neurons’ activity in real time, at the cellular level,” said Saskia de Vries, a scientist who worked on the project, at the private research center launched by Microsoft co-founder Paul Allen.
The problem the Brain Observatory wants to solve is straightforward. Science still does not understand the brain’s underlying code very well, and individual studies may turn up odd results that are difficult to interpret in the context of the whole brain.
A decade ago, for example, a widely-reported study appeared to find a single neuron in a human brain that always — and only — winked on when presented with images of Halle Berry. Few scientists suggested that this single cell actually stored the subject’s whole knowledge of Berry’s face. But without more context about what the cells around it were doing, a more complete explanation remained out of reach.
When you’re listening to a cell with an electrode, all you’re hearing is [its activity level] spiking,” said Shawn Olsen, another researcher on the project. “And you don’t know where exactly that cell is, you don’t know its precise location, you don’t know its shape, you don’t know who it connects to.
Imagine trying to assemble a complete understanding of a computer given only facts like under certain circumstances, clicking the mouse makes lights on the printer blink.
To get beyond that kind of feeling around in the dark, the Allen Institute has taken what Olsen calls an “industrial” approach to mapping out the brain’s activity.
Our goal is to systematically march through the different cortical layers, and the different cell types, and the different areas of the cortex to produce a systematic, mostly comprehensive survey of the activity,” Olsen explained. “It doesn’t just describe how one cell type is responding or one particular area, but characterizes as much as we can a complete population of cells that will allow us to draw inferences that you couldn’t describe if you were just looking at one cell at a time.
In other words, this project makes its impact through the grinding power of time and effort.
A visualization of cells examined in the project. Allen Brain Observatory

Researchers showed the mice moving horizontal or vertical lines, light and dark dots on a surface, natural scenes, and even clips from Hollywood movies.

The more abstract displays target how the mind sees and interprets light and dark, lines, and motion, building on existing neuroscience. Researchers have known for decades that particular cells appear to correspond to particular kinds of motion or shape, or positions in the visual field. This research helps them place the activity of those cells in context.
One of the most obvious results was that the brain is noisy, messy, and confusing.
Even though we showed the same image, we could get dramatically different responses from the same cell. On one trial it may have a strong response, on another it may have a weak response,” Olsen said.
All that noise in their data is one of the things that differentiates it from a typical study, de Vries said.
If you’re inserting an electrode you’re going to keep advancing until you find a cell that kind of responds the way you want it to,” he said. “By doing a survey like this we’re going to see a lot of cells that don’t respond to the stimuli in the way that we think they should. We’re realizing that the cartoon model that we have of the cortex isn’t completely accurate.

Olsen said they suspect a lot of that noise emerges from whatever the mouse is thinking about or doing that has nothing to do with what’s on screen. They recorded videos of the mice during data collection to help researchers combing their data learn more about those effects.
The best evidence for this suspicion? When they showed the mice more interesting visuals, like pictures of animals or clips from the film “Touch of Evil,” the neurons behaved much more consistently.
We would present each [clip] ten different times,” de Vries said. “And we can see from trial to trial many cells at certain times almost always respond — reliable, repeatable, robust responses.
In other words, it appears the mice were paying attention.
Allen Brain Observatory

The Brain Observatory was turned loose on the internet Wednesday, with its data available for researchers and the public to comb through, explore, and maybe critique.

But the project isn’t over.
In the next year-and-a-half, the researchers intend to add more types of cells and more regions of the visual cortex to their observatory. And their long-term ambitions are even grander.
Ultimately,” Olson said,”we want to understand how this visual information in the mouse’s brain gets used to guide behavior and memory and cognition.
Right now, the mice just watch screens. But by training them to perform tasks based on what they see, he said they hope to crack the mysteries of memory, decision-making, and problem-solving. Another parallel observatory created using electrode arrays instead of light through windows will add new levels of richness to their data.
So the underlying code of mouse — and human — brains remains largely a mystery, but the map that we’ll need to unlock it grows richer by the day.
ORIGINAL: Tech Insider

Jul. 13, 2016

Where does intelligence come from?

By Hugo Angel,

Add caption
It is amazing how intelligent we can be. We can construct shelter, find new ways of hunting, and create boats and machines. Our unique intelligence has been responsible for the emergence of civilization.
But how does a set of living cells become intelligent? How can flesh and blood turn into something that can create bicycles and airplanes or write novels?
This is the question of the origin of intelligence.
This problem has puzzled many theorists and scientists, and it is particularly important if we want to build intelligent machines. They still lag well behind us. Although computers calculate millions of times faster than we do, it is we who understand the big picture in which these calculations fit. Even animals are much more intelligent than machines. A mouse can find its way in a hostile forest and survive. This cannot be said for our computers or robots.
The question of how to achieve intelligence remains a mystery for scientists.
Recently, however a new theory has been proposed that may resolve this very question. The theory is called practopoiesis and is founded in the most fundamental capability of all biological organisms—their ability to adapt.
Darwin’s theory of evolution describes one way how our genomes adapt. By creating offspring new combinations of genes are tested; the good ones are kept and the bad ones are disposed of. The result is a genome better adapted to the environment.
Practopoiesis tells us that somewhat similar adaptation mechanisms of trials and errors occur while an organism grows, while it digests food and also, while it acts intelligently or thinks.
For example, the growth of our body is not precisely programmed by the genes. Instead, our genes perform experiments, which require feedback from the environment and corrections of errors. Only with trial and errors can our body properly grow.
Our genes contain an elaborate knowledge of which experiments need to be done, and this knowledge of trial-and-error approaches has been acquired through eons of evolution. We kept whatever worked well for our ancestors.
However, this knowledge alone is not enough to make us intelligent.
To create intelligent behavior such as thinking, decision making, understanding a poem, or simply detecting one’s friend in a crowd of strangers, our bodies require yet another type of trial-and-error knowledge. There are mechanisms in our body that also contain elaborate knowledge for experimenting, but they are much faster. The knowledge of these mechanisms is not collected through evolution but through the development over the lifetime of an individual.
These fast adaptive mechanisms continually adjust the big network of our connected nerve cells. These adaptation mechanisms can change in an eye-blink the way the brain networks are effectively connected. It may take less than a second to make a change necessary to recognize one’s own grandmother, or to make a decision, or to get a new idea on how to solve a problem.
The slow and the fast adaptive mechanisms share one thing: They cannot be successful without receiving feedback and thus iterating through several stages of trial and error; for example, testing several possibilities of who this person in distance could be.
Practopoiesis states that the slow and fast adaptive mechanisms are collectively responsible for creation of intelligence and are organized into a hierarchy. 
  • First, evolution creates genes at a painstakingly slow tempo. Then genes slowly create the mechanisms of fast adaptations
  • Next, adaptation mechanisms change the properties of our nerve cells within seconds
  • And finally, the resulting adjusted networks of nerve cells route sensory signals to muscles with the speed of lightning. 
  • At the end behavior is created.
Probably the most groundbreaking aspect of practopoietic theory is that our intelligent minds are not primarily located in the connectivity matrix of our neural networks, as it has been widely held, but instead in the elaborate knowledge of the fast adaptive mechanisms. The more knowledge our genes store into our quick abilities to adapt nerve cells, the more capability we have to adjust in novel situations, solve problems, and generally, act intelligently.
Therefore, our intelligence seems to come from the hierarchy of adaptive mechanisms, from the very slow evolution that enables the genome to adapt over a lifetime, to the quick pace of neural adaptation expressing knowledge acquired through its lifetime. Only when these adaptations have been performed successfully can our networks of neurons perform tasks with wonderful accuracy.
Our capability to survive and create originates, then, 
  • from the adaptive mechanisms that operate at different levels and 
  • the vast amounts of knowledge accumulated by each of the levels.
 The combined result of all of them together is what makes us intelligent.
May 16, 2016
Danko Nikolić
About the Author:
Danko Nikolić is a brain and mind scientist, running an electrophysiology lab at the Max Planck Institute for Brain Research, and is the creator of the concept of ideasthesia. More about practopoiesis can be read here

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

A Scale-up Synaptic Supercomputer (NS16e): Four Perspectives

By Hugo Angel,

Today, Lawrence Livermore National Lab (LLNL) and IBM announce the development of a new Scale-up Synaptic Supercomputer (NS16e) that highly integrates 16 TrueNorth Chips in a 4×4 array to deliver 16 million neurons and 256 million synapses. LLNL will also receive an end-to-end software ecosystem that consists of a simulator; a programming language; an integrated programming environment; a library of algorithms as well as applications; firmware; tools for composing neural networks for deep learning; a teaching curriculum; and cloud enablement. Also, don’t miss the story in The Wall Street Journal (sign-in required) and the perspective and a video by LLNL’s Brian Van Essen.
To provide insights into what it took to achieve this significant milestone in the history of our project, following are four intertwined perspectives from my colleagues:

  • Filipp Akopyan — First Steps to an Efficient Scalable NeuroSynaptic Supercomputer.
  • Bill Risk and Ben Shaw — Creating an Iconic Enclosure for the NS16e.
  • Jun Sawada — NS16e System as a Neural Network Development Workstation.
  • Brian Taba — How to Program a Synaptic Supercomputer.
The following timeline provides context for today’s milestone in terms of the continued evolution of our project.
Illustration Credit: William Risk

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

Bridging the Bio-Electronic Divide

By Hugo Angel,

New effort aims for fully implantable devices able to connect with up to one million neurons
A new DARPA program aims to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the human brain and the digital world. The interface would serve as a translator, converting between the electrochemical language used by neurons in the brain and the ones and zeros that constitute the language of information technology. The goal is to achieve this communications link in a biocompatible device no larger than one cubic centimeter in size, roughly the volume of two nickels stacked back to back.
The program, Neural Engineering System Design (NESD), stands to dramatically enhance research capabilities in neurotechnology and provide a foundation for new therapies.
“Today’s best brain-computer interface systems are like two supercomputers trying to talk to each other using an old 300-baud modem,” said Phillip Alvelda, the NESD program manager. “Imagine what will become possible when we upgrade our tools to really open the channel between the human brain and modern electronics.
Among the program’s potential applications are devices that could compensate for deficits in sight or hearing by feeding digital auditory or visual information into the brain at a resolution and experiential quality far higher than is possible with current technology.
Neural interfaces currently approved for human use squeeze a tremendous amount of information through just 100 channels, with each channel aggregating signals from tens of thousands of neurons at a time. The result is noisy and imprecise. In contrast, the NESD program aims to develop systems that can communicate clearly and individually with any of up to one million neurons in a given region of the brain.
Achieving the program’s ambitious goals and ensuring that the envisioned devices will have the potential to be practical outside of a research setting will require integrated breakthroughs across numerous disciplines including 
  • neuroscience, 
  • synthetic biology, 
  • low-power electronics, 
  • photonics, 
  • medical device packaging and manufacturing, systems engineering, and 
  • clinical testing.
In addition to the program’s hardware challenges, NESD researchers will be required to develop advanced mathematical and neuro-computation techniques to first transcode high-definition sensory information between electronic and cortical neuron representations and then compress and represent those data with minimal loss of fidelity and functionality.
To accelerate that integrative process, the NESD program aims to recruit a diverse roster of leading industry stakeholders willing to offer state-of-the-art prototyping and manufacturing services and intellectual property to NESD researchers on a pre-competitive basis. In later phases of the program, these partners could help transition the resulting technologies into research and commercial application spaces.
To familiarize potential participants with the technical objectives of NESD, DARPA will host a Proposers Day meeting that runs Tuesday and Wednesday, February 2-3, 2016, in Arlington, Va. The Special Notice announcing the Proposers Day meeting is available at https://www.fbo.gov/spg/ODA/DARPA/CMO/DARPA-SN-16-16/listing.html. More details about the Industry Group that will support NESD is available at https://www.fbo.gov/spg/ODA/DARPA/CMO/DARPA-SN-16-17/listing.html. A Broad Agency Announcement describing the specific capabilities sought will be forthcoming on www.fbo.gov.
NESD is part of a broader portfolio of programs within DARPA that support President Obama’s brain initiative. For more information about DARPA’s work in that domain, please visit:http://www.darpa.mil/program/our-research/darpa-and-the-brain-initiative.
ORIGINAL: DARPA
[email protected]
1/19/2016

Scientists have discovered brain networks linked to intelligence for the first time

By Hugo Angel,

Neurons Shutterstock 265323554_1024
Ralwel/Shutterstock.com
And we may even be able to manipulate them.
For the first time ever, scientists have identified clusters of genes in the brain that are believed to be linked to human intelligence.
The two clusters, called M1 and M3, are networks each consisting of hundreds of individual genes, and are thought to influence our

  • cognitive functions, including 
  • memory, 
  • attention, 
  • processing speed, and 
  • reasoning.
Most provocatively, the researchers who identified M1 and M3 say that these clusters are probably under the control of master switches that regulate how the gene networks function. If this hypothesis is correct and scientists can indeed find these switches, we might even be able to manipulate our genetic intelligence and boost our cognitive capabilities.
“We know that genetics plays a major role in intelligence but until now haven’t known which genes are relevant,said neurologist Michael Johnson, at Imperial College London in the UK. “This research highlights some of the genes involved in human intelligence, and how they interact with each other.
The researchers made their discovery by examining the brains of patients who had undergone neurosurgery for the treatment of epilepsy. They analysed thousands of genes expressed in the brain and combined the findings with two sets of data: genetic information from healthy people who had performed IQ tests, and from people with neurological disorders and intellectual disability.
Comparing the results, the researchers discovered that some of the genes that influence human intelligence in healthy people can also cause significant neurological problems if they end up mutating.
Traits such as intelligence are governed by large groups of genes working together – like a football team made up of players in different positions,said Johnson. “We used computer analysis to identify the genes in the human brain that work together to influence our cognitive ability to make new memories or sensible decisions when faced with lots of complex information. We found that some of these genes overlap with those that cause severe childhood onset epilepsy or intellectual disability.
The research, which is reported in Nature Neuroscience, is at an early stage, but the authors believe their analysis could have a significant impact – not only on how we understand and treat brain diseases, but one day perhaps altering brainpower itself.
Eventually, we hope that this sort of analysis will provide new insights into better treatments for neurodevelopmental diseases such as epilepsy, and ameliorate or treat the cognitive impairments associated with these devastating diseases,” said Johnson. “Our research suggests that it might be possible to work with these genes to modify intelligence, but that is only a theoretical possibility at the moment – we have just taken a first step along that road.
ORIGINAL: Science Alert
PETER DOCKRILL
22 DEC 2015