Hidden Computational Power Found in the Arms of Neurons

The dendritic arms of some human neurons can perform logic operations that once seemed to require whole neural networks.

The information-processing capabilities of the brain are often reported to reside in the trillions of connections that wire its neurons together. But over the past few decades, mounting research has quietly shifted some of the attention to individual neurons, which seem to shoulder much more computational responsibility than once seemed imaginable.

The latest in a long line of evidence comes from scientists’ discovery of a new type of electrical signal in the upper layers of the human cortex. Laboratory and modeling studies have already shown that tiny compartments in the dendritic arms of cortical neurons can each perform complicated operations in mathematical logic. But now it seems that individual dendritic compartments can also perform a particular computation — “exclusive OR” — that mathematical theorists had previously categorized as unsolvable by single-neuron systems.

“I believe that we’re just scratching the surface of what these neurons are really doing,” said Albert Gidon, a postdoctoral fellow at Humboldt University of Berlin and the first author of the paper that presented these findings in Science earlier this month.

The discovery marks a growing need for studies of the nervous system to consider the implications of individual neurons as extensive information processors. “Brains may be far more complicated than we think,” said Konrad Kording, a computational neuroscientist at the University of Pennsylvania, who did not participate in the recent work. It may also prompt some computer scientists to reappraise strategies for artificial neural networks, which have traditionally been built based on a view of neurons as simple, unintelligent switches.

The Limitations of Dumb Neurons

In the 1940s and ’50s, a picture began to dominate neuroscience: that of the “dumb” neuron, a simple integrator, a point in a network that merely summed up its inputs. Branched extensions of the cell, called dendrites, would receive thousands of signals from neighboring neurons — some excitatory, some inhibitory. In the body of the neuron, all those signals would be weighted and tallied, and if the total exceeded some threshold, the neuron fired a series of electrical pulses (action potentials) that directed the stimulation of adjacent neurons.

At around the same time, researchers realized that a single neuron could also function as a logic gate, akin to those in digital circuits (although it still isn’t clear how much the brain really computes this way when processing information). A neuron was effectively an AND gate, for instance, if it fired only after receiving some sufficient number of inputs.

Networks of neurons could therefore theoretically perform any computation. Still, this model of the neuron was limited. Not only were its guiding computational metaphors simplistic, but for decades, scientists lacked the experimental tools to record from the various components of a single nerve cell. “That’s essentially the neuron being collapsed into a point in space,” said Bartlett Mel, a computational neuroscientist at the University of Southern California. “It didn’t have any internal articulation of activity.” The model ignored the fact that the thousands of inputs flowing into a given neuron landed in different locations along its various dendrites. It ignored the idea (eventually confirmed) that individual dendrites might function differently from one another. And it ignored the possibility that computations might be performed by other internal structures.

But that started to change in the 1980s. Modeling work by the neuroscientist Christof Koch and others, later supported by benchtop experiments, showed that single neurons didn’t express a single or uniform voltage signal. Instead, voltage signals decreased as they moved along the dendrites into the body of the neuron, and often contributed nothing to the cell’s ultimate output.

This compartmentalization of signals meant that separate dendrites could be processing information independently of one another. “This was at odds with the point-neuron hypothesis, in which a neuron simply added everything up regardless of location,” Mel said.

That prompted Koch and other neuroscientists, including Gordon Shepherd at the Yale School of Medicine, to model how the structure of dendrites could in principle allow neurons to act not as simple logic gates, but as complex, multi-unit processing systems. They simulated how dendritic trees could host numerous logic operations, through a series of complex hypothetical mechanisms.

Later, Mel and several colleagues looked more closely at how the cell might be managing multiple inputs within its individual dendrites. What they found surprised them: The dendrites generated local spikes, had their own nonlinear input-output curves and had their own activation thresholds, distinct from those of the neuron as a whole. The dendrites themselves could act as AND gates, or as a host of other computing devices.

Mel, along with his former graduate student Yiota Poirazi (now a computational neuroscientist at the Institute of Molecular Biology and Biotechnology in Greece), realized that this meant that they could conceive of a single neuron as a two-layer network. The dendrites would serve as nonlinear computing subunits, collecting inputs and spitting out intermediate outputs. Those signals would then get combined in the cell body, which would determine how the neuron as a whole would respond.

Whether the activity at the dendritic level actually influenced the neuron’s firing and the activity of neighboring neurons was still unclear. But regardless, that local processing might prepare or condition the system to respond differently to future inputs or help wire it in new ways, according to Shepherd.

Whatever the case, “the trend then was, ‘OK, be careful, the neuron might be more powerful than you thought,’” Mel said.

Shepherd agreed. “Much of the power of the processing that takes place in the cortex is actually subthreshold,” he said. “A single-neuron system can be more than just one integrative system. It can be two layers, or even more.” In theory, almost any imaginable computation might be performed by one neuron with enough dendrites, each capable of performing its own nonlinear operation.

In the recent Science paper, the researchers took this idea one step further: They suggested that a single dendritic compartment might be able to perform these complex computations all on its own.

Unexpected Spikes and Old Obstacles

Matthew Larkum, a neuroscientist at Humboldt, and his team started looking at dendrites with a different question in mind. Because dendritic activity had been studied primarily in rodents, the researchers wanted to investigate how electrical signaling might be different in human neurons, which have much longer dendrites. They obtained slices of brain tissue from layers 2 and 3 of the human cortex, which contain particularly large neurons with many dendrites. When they stimulated those dendrites with an electrical current, they noticed something strange.

They saw unexpected, repeated spiking — and those spikes seemed completely unlike other known kinds of neural signaling. They were particularly rapid and brief, like action potentials, and arose from fluxes of calcium ions. This was noteworthy because conventional action potentials are usually caused by sodium and potassium ions. And while calcium-induced signaling had been previously observed in rodent dendrites, those spikes tended to last much longer.

Stranger still, feeding more electrical stimulation into the dendrites lowered the intensity of the neuron’s firing instead of increasing it. “Suddenly, we stimulate more and we get less,” Gidon said. “That caught our eye.”

To figure out what the new kind of spiking might be doing, the scientists teamed up with Poirazi and a researcher in her lab in Greece, Athanasia Papoutsi, who jointly created a model to reflect the neurons’ behavior.

The model found that the dendrite spiked in response to two separate inputs — but failed to do so when those inputs were combined. This was equivalent to a nonlinear computation known as exclusive OR (or XOR), which yields a binary output of 1 if one (but only one) of the inputs is 1.

This finding immediately struck a chord with the computer science community. XOR functions were for many years deemed impossible in single neurons: In their 1969 book Perceptrons, the computer scientists Marvin Minsky and Seymour Papert offered a proof that single-layer artificial networks could not perform XOR. That conclusion was so devastating that many computer scientists blamed it for the doldrums that neural network research fell into until the 1980s.

Neural network researchers did eventually find ways of dodging the obstacle that Minsky and Papert identified, and neuroscientists found examples of those solutions in nature. For example, Poirazi already knew XOR was possible in a single neuron: Just two dendrites together could achieve it. But in these new experiments, she and her colleagues were offering a plausible biophysical mechanism to facilitate it — in a single dendrite.

“For me, it’s another degree of flexibility that the system has,” Poirazi said. “It just shows you that this system has many different ways of computing.” Still, she points out that if a single neuron could already solve this kind of problem, “why would the system go to all the trouble to come up with more complicated units inside the neuron?”

Processors Within Processors

Certainly not all neurons are like that. According to Gidon, there are plenty of smaller, point-like neurons in other parts of the brain. Presumably, then, this neural complexity exists for a reason. So why do single compartments within a neuron need the capacity to do what the entire neuron, or a small network of neurons, can do just fine? The obvious possibility is that a neuron behaving like a multilayered network has much more processing power and can therefore learn or store more. “Maybe you have a deep network within a single neuron,” Poirazi said. “And that’s much more powerful in terms of learning difficult problems, in terms of cognition.”

Perhaps, Kording added, “a single neuron may be able to compute truly complex functions. For example, it might, by itself, be able to recognize an object.” Having such powerful individual neurons, according to Poirazi, might also help the brain conserve energy.

Larkum’s group plans to search for similar signals in the dendrites of rodents and other animals, to determine whether this computational ability is unique to humans. They also want to move beyond the scope of their model to associate the neural activity they observed with actual behavior. Meanwhile, Poirazi now hopes to compare the computations in these dendrites to what happens in a network of neurons, to suss out any advantages the former might have. This will include testing for other types of logic operations and exploring how those operations might contribute to learning or memory. “Until we map this out, we can’t really tell how powerful this discovery is,” Poirazi said.

Though there’s still much work to be done, the researchers believe these findings mark a need to rethink how they model the brain and its broader functions. Focusing on the connectivity of different neurons and brain regions won’t be enough.

The new results also seem poised to influence questions in the machine learning and artificial intelligence fields. Artificial neural networks rely on the point model, treating neurons as nodes that tally inputs and pass the sum through an activity function. “Very few people have taken seriously the notion that a single neuron could be a complex computational device,” said Gary Marcus, a cognitive scientist at New York University and an outspoken skeptic of some claims made for deep learning.

Although the Science paper is but one finding in an extensive history of work that demonstrates this idea, he added, computer scientists might be more responsive to it because it frames the issue in terms of the XOR problem that dogged neural network research for so long. “It’s saying, we really need to think about this,” Marcus said. “The whole game — to come up with how you get smart cognition out of dumb neurons — might be wrong.”

“This is a super clean demonstration of that,” he added. “It’s going to speak above the noise.”

Link original: https://www.quantamagazine.org/neural-dendrites-reveal-their-computational-power-20200114/?fbclid=IwAR1dAkgDLGmaVYkGz1dkmElS3ZvtC89ZGYn9EyDr9hpqU1ocZwasvN_pRC0


The human brain builds structures in 11 dimensions, discover scientists

 

 

 

 

 

The brain continues to surprise us with its magnificent complexity. Groundbreaking research that combines neuroscience with math tells us that our brain creates neural structures with up to 11 dimensions when it processes information. By “dimensions,” they mean abstract mathematical spaces, not other physical realms. Still, the researchers “found a world that we had never imagined,” said Henry Markram, director of the Blue Brain Project, which made the discovery.

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Mood neurons mature during adolescence

Researchers have discovered a mysterious group of neurons in the amygdala — a key center for emotional processing in the brain — that stay in an immature, prenatal developmental state throughout childhood. Most of these cells mature rapidly during adolescence, suggesting a key role in the brain’s emotional development, but some stay immature throughout life, suggesting new ideas about how the brain keeps its emotional responses flexible throughout life.

“Most brain cells have matured far beyond this stage by the time you are born,” said study lead author Shawn Sorrells, PhD, a former UCSF researcher who is now assistant professor of neuroscience at the University of Pittsburgh. “It’s fascinating that these are some of the very last cells to mature in the human brain, and most do so during puberty, precisely when huge developments in emotional intelligence are going on.”

The amygdala is an almond-shaped brain structure located deep in the brain’s temporal lobes (you actually have two, one on each side of the brain) that plays a key role in learning appropriate emotional responses to our environment. During childhood and adolescence — long after most of the rest of the human brain is finished growing — the amygdala continues to expand by as many as two million neurons, a late growth spurt that researchers believe is likely to play a key role in human emotional development, and which may go awry in neurodevelopmental disorders. For example, this expansion is absent in children with autism, and mood disorders that frequently emerge in adolescence, such as depression, anxiety, bipolar disorder, and post-traumatic stress disorder (PTSD), have also been linked to problems with amygdala development.

Recent studies had detected a unique group of immature neurons in a region of the amygdala called the paralaminar nuclei (PL), which could help explain the amygdala’s rapid growth, but researchers had little idea where these cells came from or what role they play in mature brain circuits — even whether they are excitatory or inhibitory, the two main functional classes of neurons.

In the new study, published June 21, 2019, in Nature Communications, researchers from the lab of Arturo Alvarez-Buylla, PhD, the Heather and Melanie Muss Endowed Chair and Professor of Neurological Surgery and a member of the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research at UCSF, set out to understand the identity of these cells and their role in the amygdala’s rapid growth during childhood.

The researchers examined postmortem human amygdala tissue from 49 human brains — ranging in age from 20 gestational weeks to 78 years of age. Using both anatomical and molecular techniques to classify individual neurons’ maturity and function within neural circults, they found that the percentage of immature cells in the PL region of the amygdala remains high throughout childhood, but declines rapidly during adolescence: from birth to age 13, the number of immature cells declines from approximately 90 percent to just under 70 percent, but by the end of adolescence, only about 20 percent of PL cells remain immature.

Based on quantification of neurons in different stages of development coupled with analysis of gene expression patterns in individual neurons extracted from PL, the researchers showed that as the immature cells disappear, they are replaced by mature excitatory neurons — suggesting that the cells have taken their place in the amygdala’s maturing emotion processing circuitry. Since this is the first time these neurons have been clearly studied, scientists don’t know exactly what function the neurons serve, but the timing of their maturation suggests they may play a role in the rapid emotional development that occurs during human adolescence.

“Anyone who’s met a teenager knows that they are going through a rapid and sometimes tumultuous process of emotional learning about how to respond to stress, how to form positive social bonds, and so on,” Sorrells said. “At the same time, adolescence is when many psychiatric disorders known to involve the amygdala first manifest, suggesting that perhaps something has gone wrong with the normal process of emotional and cognitive development — though whether these cells are involved is a matter for future study.”

Notably, the researchers also found that some immature neurons appear to remain in the amygdala throughout life, and were even found in one 77-year-old brain. These results were in stark contrast to the hippocampus — a nearby structure in which the authors recently found that newborn and immature neurons completely decline to undetectable levels by adolescence.

“This is consistent with what we have seen before: that immature neurons are vanishingly rare in the adult hippocampus, but they do appear to persist in the amygdala,” Alvarez-Buylla said. “As far as we can tell, these cells aren’t being born throughout life, but seem to be maintained in an immature state from birth, though we can’t say this for sure given the techniques we’ve used here.”

In other animals, such as mice, new neurons continue to be born throughout life in the memory-forming hippocampus — and possibly at low rates in the amygdala — which researchers believe allows the brain to continuously rewire neural circuits to adapt to new experiences and environments. Following on the authors’ 2018 study showing that the birth of new neurons declines in the human brain during childhood and is very rare or absent in adults, the new study suggests that the human brain may maintain reserves of immature neurons throughout life, using these “Peter Pan” cells in a similar manner to the neurogenesis seen in other species — as new cells to be called on as needed to keep the brain’s emotional responses flexible and adaptable into old age.

“You could imagine these immature cells let the brain continue to sculpt the structure of neural circuits and their growth once you are out in the world experiencing what it’s like,” Sorrells said. “Of course, that’s just speculation at this point — one of the fascinating questions these findings open up for future study.”

Neurogenesis

Whether new neurons are born in the adult primate or human brain remains controversial. In 2018, Alvarez-Buylla, Sorrells and colleagues published results of the most rigorous search yet for new neurons in the human hippocampus, and they found that the birth of new neurons declined rapidly in childhood and was undetectable in adults.

Subsequently, other groups published data that appears to show newborn neurons in the adult human hippocampus, but Alvarez-Buylla and colleagues believe these studies rely too strongly on a small number of molecular markers for newborn neurons. They have shown that these markers can also be found in fully mature neurons and in non-neuronal cells called glia — which are known to continue dividing throughout life.

“Identifying new neurons is technically very challenging,” Alvarez-Buylla said. “It’s easy to forget that the molecular markers we use to identify particular molecules are not produced for our benefit — cells are using these molecules for their own biological needs, which are always going to be messy from the perspective of someone looking for simple classification. This is why we have endeavored to examine as many lines of evidence as possible — not just molecular markers but also cells’ shape and appearance — to make sure we are confident in what types of cells we are actually looking at in these analyses.”

The new study in the amygdala uses comprehensive single-cell gene expression techniques to sensitively detect immature neurons based on multiple lines of molecular evidence, and reinforces the group’s earlier findings in the hippocampus — showing that the precursors that divide to give birth to new neurons disappear within the first two years of life in the human amygdala, and that most immature neurons disappear during adolescence.

“Single-cell sequencing not only clearly identifies these long-lived immature neurons, but also shows that they express many developmental genes involved in axon development, synaptogenesis, dendrite morphogenesis, and even neuronal migration,” Sorrells said. “These cells could be erroneously assumed to be newborn neurons, but based on our developmental perspective, and the fact that we see few dividing cells present nearby, it looks as though they are already present at birth and decline throughout life.”

Link Original: https://www.sciencedaily.com/releases/2019/06/190624111530.htm?fbclid=IwAR2jYCOMWio-4CILDE2my2ppYPBSe2aK23eyeI2bt5S2ViqyVOhX-8VA4LE


Can CBD Really Do All That?

 

 

 

 

 

 

 

 

 

When Catherine Jacobson first heard about the promise of cannabis, she was at wits’ end. Her 3-year-old son, Ben, had suffered from epileptic seizures since he was 3 months old, a result of a brain malformation called polymicrogyria. Over the years, Jacobson and her husband, Aaron, have tried giving him at least 16 different drugs, but none provided lasting relief. They lived with the grim prognosis that their son — whose cognitive abilities never advanced beyond those of a 1-year-old — would likely continue to endure seizures until the cumulative brain injuries led to his death.

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