Physicists Overturn a 100-Year-Old Assumption on How Brains Work


The human brain contains a little over 80-odd billion neurons, each joining with other cells to create trillions of connections called synapses.

The numbers are mind-boggling, but the way each individual nerve cell contributes to the brain’s functions is still an area of contention. A new study has overturned a hundred-year-old assumption on what exactly makes a neuron ‘fire’, posing new mechanisms behind certain neurological disorders.

A team of physicists from Bar-Ilan University in Israel conducted experiments on rat neurons grown in a culture to determine exactly how a neuron responds to the signals it receives from other cells.

To understand why this is important, we need to go back to 1907 when a French neuroscientist named Louis Lapicque proposed a model to describe how the voltage of a nerve cell’s membrane increases as a current is applied.

Once reaching a certain threshold, the neuron reacts with a spike of activity, after which the membrane’s voltage resets.

What this means is a neuron won’t send a message unless it collects a strong enough signal.

Lapique’s equations weren’t the last word on the matter, not by far. But the basic principle of his integrate-and-fire model has remained relatively unchallenged in subsequent descriptions, today forming the foundation of most neuronal computational schemes.

Image credit: NICHD/Flickr

According to the researchers, the lengthy history of the idea has meant few have bothered to question whether it’s accurate.

“We reached this conclusion using a new experimental setup, but in principle these results could have been discovered using technology that has existed since the 1980s,” says lead researcher Ido Kanter.

“The belief that has been rooted in the scientific world for 100 years resulted in this delay of several decades.”

The experiments approached the question from two angles – one exploring the nature of the activity spike based on exactly where the current was applied to a neuron, the other looking at the effect multiple inputs had on a nerve’s firing.

Their results suggest the direction of a received signal can make all the difference in how a neuron responds.

A weak signal from the left arriving with a weak signal from the right won’t combine to build a voltage that kicks off a spike of activity. But a single strong signal from a particular direction can result in a message.

This potentially new way of describing what’s known as spatial summation could lead to a novel method of categorising neurons, one that sorts them based on how they compute incoming signals or how fine their resolution is, based on a particular direction.

Better yet, it could even lead to discoveries that explain certain neurological disorders.

It’s important not to throw out a century of wisdom on the topic on the back of a single study. The researchers also admit they’ve only looked at a type of nerve cell called pyramidal neurons, leaving plenty of room for future experiments.

But fine-tuning our understanding of how individual units combine to produce complex behaviours could spread into other areas of research. With neural networks inspiring future computational technology, identifying any new talents in brain cells could have some rather interesting applications.

Physicists Overturn a 100-Year-Old Assumption on How Brains Work


This is how neurons actually fire.

The human brain contains a little over 80-odd billion neurons, each joining with other cells to create trillions of connections called synapses.

The numbers are mind-boggling, but the way each individual nerve cell contributes to the brain’s functions is still an area of contention. A new study has overturned a hundred-year-old assumption on what exactly makes a neuron ‘fire’, posing new mechanisms behind certain neurological disorders.

A team of physicists from Bar-Ilan University in Israel conducted experiments on rat neurons grown in a culture to determine exactly how a neuron responds to the signals it receives from other cells.

To understand why this is important, we need to go back to 1907 when a French neuroscientist named Louis Lapicque proposed a model to describe how the voltage of a nerve cell’s membrane increases as a current is applied.

Once reaching a certain threshold, the neuron reacts with a spike of activity, after which the membrane’s voltage resets.

What this means is a neuron won’t send a message unless it collects a strong enough signal.

Lapique’s equations weren’t the last word on the matter, not by far. But the basic principle of his integrate-and-fire model has remained relatively unchallenged in subsequent descriptions, today forming the foundation of most neuronal computational schemes.

According to the researchers, the lengthy history of the idea has meant few have bothered to question whether it’s accurate.

“We reached this conclusion using a new experimental setup, but in principle these results could have been discovered using technology that has existed since the 1980s,” says lead researcher Ido Kanter.

“The belief that has been rooted in the scientific world for 100 years resulted in this delay of several decades.”

The experiments approached the question from two angles – one exploring the nature of the activity spike based on exactly where the current was applied to a neuron, the other looking at the effect multiple inputs had on a nerve’s firing.

Their results suggest the direction of a received signal can make all the difference in how a neuron responds.

A weak signal from the left arriving with a weak signal from the right won’t combine to build a voltage that kicks off a spike of activity. But a single strong signal from a particular direction can result in a message.

This potentially new way of describing what’s known as spatial summation could lead to a novel method of categorising neurons, one that sorts them based on how they compute incoming signals or how fine their resolution is, based on a particular direction.

Better yet, it could even lead to discoveries that explain certain neurological disorders.

It’s important not to throw out a century of wisdom on the topic on the back of a single study. The researchers also admit they’ve only looked at a type of nerve cell called pyramidal neurons, leaving plenty of room for future experiments.

But fine-tuning our understanding of how individual units combine to produce complex behaviours could spread into other areas of research. With neural networks inspiring future computational technology, identifying any new talents in brain cells could have some rather interesting applications.

Human brain can store 4.7 billion books – ten times more than originally thought


One petabyte is the same as 20 million four-drawer filing cabinets filled with text,13.3 years of HD-TV recordings, 4.7 billion books or 670 million web pages.

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books, scientists have discovered.

This is according to US scientists who have measured the storage capacity of synapses – the brain connections that are responsible for storing memories.

They discovered that, on average, one synapse can hold roughly 4.7 bits of information. This means that the human brain has a capacity of one petabyte, or 1,000,000,000,000,000 bytes.

One petabyte is the same as 20 million four-drawer filing cabinets filled with text,13.3 years of HD-TV recordings, 4.7 billion books or 670 million web pages.

“The discovery is a real bombshell in the field of neuroscience,”
Professor Terry Sejnowski

Nevertheless, Professor Terry Sejnowski, of the Salk Institute for Biological Studies, in California, said the discovery is a “real bombshell in the field of neuroscience”.

“We discovered the key to unlocking the design principle for how hippocampal neurons function with low energy but high computation power,” he said.

“Our new measurements of the brain’s memory capacity increase conservative estimates by a factor of 10 to at least a petabyte, in the same ballpark as the World Wide Web.”

Source:http://www.telegraph.co.uk

Scientists think they have another reason humans became smarter than our ancestors blood.


It’s not just about brain size.

Anthropologists have been curious about the evolution of human intelligence for many decades. The main lines of research have involved archaeological finds concerning the use of fire, tools and so on. The Conversation

But what about looking for evidence in fossil skulls, the place where the brain resided?

The volume of the human brain increased to be about three and a half times larger than our Australopithecus ancestors 3 million years ago.

It is generally assumed that intelligence is correlated with brain size, and the reason for this is that the number of nerve cells in mammalian brains seems to be directly related to brain size.

Our research focused on the rate of blood flow to the brain, which relates closely to metabolic rate because the blood supplies the essential oxygen. If blood flow to your brain is stopped, you will pass out within seconds.

Normally you have about 7 millilitres of blood flowing to your brain each second. Remarkably, this rate changes little, regardless of whether you are awake, asleep or solving mathematical problems.

The brain’s plumbing

The blood flow to the cognitive part of the brain, the cerebrum, comes through two internal carotid arteries, one on the right and one on the left. The size of these arteries is related to the rate of blood flow through them.

 Just as a plumber would install larger water pipes to accommodate a higher flow rate to a larger building, the blood circulatory system continually adjusts the sizes of blood vessels to match the rate of blood flow inside them.

This in turn is related to the oxygen demand of the organ.

If we can measure the size of the large arteries that supply an organ such as the brain, we can calculate the average rate of blood flow with some accuracy.

This principle has been known for a century and its beauty lies in its simplicity.

Size matters

My eureka moment occurred when I realised that the size of an artery can be gauged by the size of the hole in a bone that it passes through.

This meant that the rate of blood flow to the brain could be measured by the sizes of the carotid canals in fossil skulls from human evolution.

It was a nice idea, but it took the enthusiasm of my student Vanya Bosiocic to turn it into a piece of research.

She travelled to museums in Australia and in South Africa, gaining access to priceless fossil hominin skulls to make the measurements.

We found that the size of the carotid canals increased much faster than expected from brain size in 12 species of our human ancestors over a period of 3 million years.

While brain size was increasing 3.5 times, blood flow rate surprisingly increased sixfold, from about 1.2 millilitre per second to 7 millilitre per second.

This indicates that our brains are six times as hungry for oxygen as those of our ancestors, presumably because our cognitive ability is greater and therefore more energy-intensive.

Because the number of nerve cells (neurons) in human brains seems to be roughly as expected for a larger primate brain, our discovery implies that the brain’s substance is more active, probably because there are more connections between the neurons.

Each connection, called a synapse, operates to transmit electrical impulses from one cell to another, usually by the release of a chemical substance from one cell that stimulates or inhibits the production of impulses in another cell.

The cycling of the substances between impulses costs a tiny amount of energy. But considering that the brain contains 80 billion nerve cells and each one has thousands of synapses with other cells, the energy cost mounts up.

The human computer

The human body allocates 20-25 percent of its total resting metabolic rate to the brain, compared with 8-10 percent in other primates and a mere 3-5 percent in other mammals.

Thus we view the brain as a rather energy-hungry supercomputer.

This analogy with an electrical computer is a good one. The greater a computer’s capacity, the more electrical power is required to keep it running, and the larger the electrical supply cables need to be.

It is the same with the brain. The higher the cognitive function, the higher the metabolic rate, the greater the blood flow and the larger the arteries.

The evolution of the human brain is unique among animals. We have looked at the size of the carotid arteries in 34 species of living primates that represent evolution toward the great apes and hominins.

Among these representatives of primate evolution, both body size and brain size increased, but body size increased faster. The blood flow to primate brains increased roughly in proportion to brain size.

Only in the hominins do we see that blood flow increased faster than brain size, which indicates that the brain was not only developing in size, but in usage as well. And that shows our ancestors were getting smarter.

Source:sciencealert.com

Engineers Have Created Artificial Synapses That Mimic the Human Brain


IN BRIEF
  • The device relies on “memristors,’ components whose resistance relies on how much charge has passed through them in the past.
  • One brain has roughly one quadrillion of these synaptic connections

EMULATING SYNAPSES

Some scientists suggest that, instead of working on artificial intelligence that functions better than the human brain, we should be making computers like the brain. We may well be on this path after engineers from University of Massachusetts Amherst demonstrated devices that emulate the behavior of the brain’s synapses.

Their device uses “memristors,’ components whose resistance relies on how much charge has passed through them in the past. That means they have the ability to store and process information, and have some characteristics that make them better than traditional integrated circuits.

These memristors have been used before, but what makes this study unique is that two different kinds of memristors are being combined to better emulate the brain. On their own, memristors have been made to mimic synapses, using electrical fields. But these are based on physical processes, not biological ones. When used with diffusion-type memristors, however, the whole set up becomes more like how a regular synapse fires up.

“In the past, people have used devices like transistors and capacitors to simulate synaptic dynamics, which can work, but those devices have very little resemblance to real biological systems. So it’s not efficient to do it that way, and it results in a larger device area, larger energy consumption and less fidelity,” said study leader Joshua Yang, a professor of electrical and computer engineering at the University of Massachusetts Amherst.

THE BEST COMPUTER

If scientists can successfully replicate the synaptic functions of the brain, they are closer to that brain emulating computer. Just one brain has approximately 100 billion neurons and roughly one quadrillion of these synaptic connections.

While scientists have created computers as fast as the brain (the human brain can perform about 10 quadrillion operations per second, the world’s fastest supercomputer, Tianhe-2, can carry out up to about 55 quadrillion calculations per second), they have yet to create efficient ones (the brain needs just enough energy to light a lightbulb).

With that kind of power and efficiency, everything running on computers would get a boost, whether it be robots, self-driving cars, AI, data analysis, etc. In fact, some scientists see brain emulation as the key to achieving the Singularity.

Human brain can store 4.7 billion books – ten times more than originally thought


One petabyte is the same as 20 million four-drawer filing cabinets filled with text,13.3 years of HD-TV recordings, 4.7 billion books or 670 million web pages

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books.
“The discovery is a real bombshell in the field of neuroscience,”
Professor Terry Sejnowski

The human brain has a capacity that is ten times greater than first thought and can retain 4.7 billion books, scientists have discovered.

This is according to US scientists who have measured the storage capacity of synapses – the brain connections that are responsible for storing memories.

They discovered that, on average, one synapse can hold roughly 4.7 bits of information. This means that the human brain has a capacity of one petabyte, or 1,000,000,000,000,000 bytes.

One petabyte is the same as 20 million four-drawer filing cabinets filled with text,13.3 years of HD-TV recordings, 4.7 billion books or 670 million web pages.

However, this is only the total amount of information that the relevant part of the brain could theoretically carry at any one moment. Its actual archive of memories would be a lot smaller.

Nevertheless, Professor Terry Sejnowski, of the Salk Institute for Biological Studies, in California, said the discovery is a “real bombshell in the field of neuroscience”.

“We discovered the key to unlocking the design principle for how hippocampal neurons function with low energy but high computation power,” he said.

“Our new measurements of the brain’s memory capacity increase conservative estimates by a factor of 10 to at least a petabyte, in the same ballpark as the World Wide Web.”

HUMAN BRAIN SUCKED UP MUSCLE GROWTH


In order to fuel our cognitive skills, research shows our bodies relayed all the energy from muscle growth to brain development.

 While we battle it out every day in the gym and pay with our sweat and tears to build muscle, a new study suggests that over the course of evolution, the human body actually eschewed muscle growth to fuel brain development.
Study: Human Brain Sucked up Muscle Growth
“For a long time we were confused by metabolic changes in human muscle, until we realized that what other primates have in common, in contrast to humans, is their enormous muscle strength,” said Dr. Kasia Bozek, the first author of the study, which is called Exceptional Evolutionary Divergence of Human Muscle and Brain Metabolomes Parallels Human Cognitive and Physical Uniqueness.

Our nearest animal relative is the chimpanzee, and the research claims the essential molecules in human muscle have changed 10 times more than cousin chimp over evolution, while the metabolome (dictionary definition: “the full complement of metabolites present in a cell, tissue, or organism in a particular physiological or developmental state”) in the human brain changed four times faster.

“Our results suggest a special energy management in humans that allows us to spare energy for our extraordinary cognitive powers at a cost of weak muscle,” said Bozek. To rule out the lazy-humans-who-eat-crap-and-don’t-move-factor, researchers took active outdoor macaque monkeys and brought them into confined spaces and fed them foods filled with fat and sugar (they turned them into slobs), but the significant change in setting and lifestyle had a minimal impact on the metabolome of the monkeys tested.

Yep, it would appear that we expanded a lot of our energy on the development of our brains over time and our muscle growth paid the bill. It’s a bit on a conundrum for the M&Fer, who is supremely motivated to build muscle, but if we had not gone down this evolutionary path, then we might have missed out on, well, civilization as we know it. So deal with it. Or, if you feel that you want to redress the strength gap between humans and chimpanzees (the research also showed they chimps can lift over double what humans can) then we suggest you head to our workouts section and get busy!

IBM is one step closer to mimicking the human brain


A breakthrough in cognitive computing has enabled scientists to imitate large populations of neurons.

Scientists at IBM have claimed a computational breakthrough after imitating large populations of neurons for the first time.

Neurons are electrically excitable cells that process and transmit information in our brains through electrical and chemical signals. These signals are passed over synapses, specialised connections with other cells.

It’s this set-up that inspired scientists at IBM to try and mirror the way the biological brain functions using phase-change materials for memory applications.

Using computers to try to mimic the human brain is something that’s been theorised for decades due to the challenges of recreating the density and power. Now, for the first time, scientists have created their own “randomly spiking” artificial neurons that can store and process data.

“The breakthrough marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing,” the scientists said.

The artificial neurons consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one (without a clearly defined structure) and a crystalline one (with structure). These materials are also the basis of re-writable Blue-ray but in this system the artificial neurons do not store digital information; they are analogue, just like the synapses and neurons in a biological brain.

The beauty of these powerful phase-change-based artificial neurons, which can perform various computational primitives such as data-correlation detection and unsupervised learning at high speeds, is that they use very little energy – just like human brain.

In a demonstration published in the journal Nature Nanotechnology, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallisation of the phase-change material, ultimately causing the neuron to fire.

In neuroscience, this function is known as the integrate-and-fire property of biological neurons. This is the foundation for event-based computation and, in principle, is quite similar to how a biological brain triggers a response when an animal touches something hot, for instance.

“Populations of stochastic phase-change neurons, combined with other nanoscale computational elements such as artificial synapses, could be a key enabler for the creation of a new generation of extremely dense neuromorphic computing systems,” said Tomas Tuma, co-author of the paper.

This could be useful in sensors collecting and analysing volumes of weather data, for instance, said Sebastian, collected at the edge, in remote locations, for faster and more accurate weather forecasts.

The artificial neurons could also detect patterns in financial transactions to find discrepancies or use data from social media to discover new cultural trends in real time. While large populations of these high-speed, low-energy nano-scale neurons could also be used in neuromorphic co-processors with co-located memory and processing units.

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How smartphone’s light affect human brain?


TI_Graphics_How blue light affects body (1)

It may be hard to stop, but looking at your phone at night is a terrible idea.

Smartphone screens emit bright blue light so you can see them even at the sunniest times of day.

But at night, your brain gets confused by that light, as it mimics the brightness of the sun. This causes the brain to stop producing melatonin, a hormone that gives your body the “time to sleep” cues. Because of this, smartphone light can disrupt your sleep cycle, making it harder to fall and stay asleep — and potentially causing serious health problems along the way.

Handle With Care: The Human Brain Is Delicate And You Should Take Care Of It


humanbrain

Your brain is softer than most of the meat you’d find in a grocery store. So treat it nicely. University of Utah Brain Institute, YouTube/screenshot

It’s easy to see a 3-year-old run himself into his bedroom wall, shake it off, and go play in another room, and think the brain is indestructible. Or maybe you see hulking grown men hurling themselves at one another on the football field and chalk up their lasting health to their athleticism. But one neurobiology professor would like to disagree.

Dr. Suzanne Stensaas, of the University of Utah School of Medicine, has a freshly autopsied brain that she’d like you to take a look at. Stensaas also teaches anatomy, which means she can name just about every nook and cranny of the brain’s myriad folds and pathways. As she turns over the three-pound mass in her hands, she reflects on how soft it really is. Not even a minute of it resting in her hands and one finger already leaves a dent in the tissue.

“Think how vulnerable the brain is, and then think how narrow and small the spinal cord is and how devastating a quick subluxation of the vertebra, or a herniation of a disk,” would be to the delicate spinal cord, Stensass explains. “It’s much softer than most of the meat you would see in a market.”

Should it be any wonder that concussions and other traumatic brain injuries are so common in high-impact sports? Cerebrospinal fluid acts as a protective cushion around the brain, but nothing can stop the organ from bashing into the inner walls of the skull after quickly stopping at a high velocity — no matter how advanced a helmet (or 3-year-old sense of limitlessness) may be.

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