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 secret to regeneration? Scientists say it lies in the axolotl genome.

Few creatures have captured the attention of both the general public and scientists as thoroughly as a peculiar-looking salamander known as the axolotl. Native only to Lake Xochimilco, south of Mexico City, axolotls are less and less frequently found in the wild. However, they are relatively abundant in captivity, with pet enthusiasts raising them due to their alien features, such as the striking, fringy crown they wear on their heads. Researchers also keep a large supply of axolotl in captivity due to the many unique properties that make them attractive subjects of study.

Perhaps the most notable and potentially useful of these characteristics is the axolotl’s uncanny ability to regenerate. Unlike humans and other animals, axolotls don’t heal large wounds with the fibrous tissue that composes scars. Instead, they simply regrow their injured part. 

“It regenerates almost anything after almost any injury that doesn’t kill it,” said Yale researcher Parker Flowers in a statement. This capability is remarkably robust, even for salamanders. Where regular salamanders are known to regrow lost limbs, axolotls have been observed regenerating ovaries, lung tissues, eyes, and even parts of the brain and spinal cord.

Obviously, figuring out how these alien-looking salamanders manage this magic trick is of great interest to researchers. Doing so could reveal a method for providing humans with a similar regenerative capability. But identifying the genes involved in this process has been tricky — the axolotl has a genome 10 times larger than that of a human’s, making it the largest animal genome sequenced to date.

Fortunately, Flowers and colleagues recently discovered a means of more easily navigating this massive genome and, in the process, identified two genes involved in the axolotl’s remarkable regenerative capacity.

A new role for two genes

We’ve understood the basic process of regeneration in axolotls for a while now. After a limb is severed, for instance, blood cells clot at the site, and skin cells start to divide and cover the exposed wound. Then, nearby cells begin to travel to the site and congregate in a blob called the blastema. The blastema then begins to differentiate into the cells needed to grow the relevant body part and grow outward according to the appropriate limb structure, resulting in a new limb identical to its severed predecessor.

But identifying which genes code for this process and what mechanisms guide its actions is less clear. Building off of previous work using CRISPR/Cas9, Flowers and colleagues were able to imprint regenerated cells with a kind of genetic barcode that enabled them to trace the cells back to their governing genes. In this way, they were able to identify and track 25 genes suspected to be involved in the regeneration process. From these 25, they identified two genes related to the axolotls’ tail regeneration; specifically, the catalase and fetub genes.

Although the researchers stressed that many more genes were likely driving this complicated process, the finding does have important implications for human beings — namely that humans also possess similar genes to the two identified in this study. Despite sharing similar genes, the same gene can do very different work across species and within a single animal. The human equivalent gene FETUB, for example, produces proteins that regulate bone resorption, regulates insulin and hepatocyte growth factor receptors, responds to inflammation, and more. In the axolotl, it appears that regulating the regenerative process is another duty.

Since humans possess the same genes that enable axolotls to regenerate, researchers are optimistic that one day we will be able to speed up wound healing or even to completely replicate the axolotl’s incredible ability to regenerate organs and limbs. With continued research such as this, it’s only a matter of time until this strange salamander gives ups its secrets.

Link Original: https://bigthink.com/surprising-science/axolotl-regeneration?rebelltitem=1#rebelltitem1

New invention promises quantum internet that can’t be hacked

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  • Scientists devise the largest-ever quantum communications network.
  • The technology is much cheaper than previous attempts and promises to be hacker-proof.
  • The ‘multiplexing’ system devised by the researchers splits light particles that carry information.

Scientists are closer to creating a hacker-proof quantum internet thanks to a promising new invention. A team led by the University of Bristol in the U.K. found a method of securing online communication that relies on the laws of physics.

The approach aims to make any message sent over the internet interception-proof. 

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Brain changed by caffeine in utero

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New research finds caffeine consumed during pregnancy can change important brain pathways in baby

Date:February 8, 2021Source:University of Rochester Medical CenterSummary:New research finds caffeine consumed during pregnancy can change important brain pathways that could lead to behavioral problems later in life. Researchers analyzed thousands of brain scans of nine and ten-year-olds, and revealed changes in the brain structure in children who were exposed to caffeine in utero.

New research finds caffeine consumed during pregnancy can change important brain pathways that could lead to behavioral problems later in life. Researchers in the Del Monte Institute for Neuroscience at the University of Rochester Medical Center (URMC) analyzed thousands of brain scans of nine and ten-year-olds, and revealed changes in the brain structure in children who were exposed to caffeine in utero.

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Quantum Theory Proposes That Cause and Effect Can Go In Loops

Causality is one of those difficult scientific topics that can easily stray into the realm of philosophy.  Science’s relationship with the concept started out simply enough: an event causes another event later in time.  That had been the standard understanding of the scientific community up until quantum mechanics was introduced.  Then, with the introduction of the famous “spooky action at a distance” that is a side effect of the concept of quantum entanglement, scientists began to question that simple interpretation of causality

Now, researchers at the Université Libre de Bruxelles (ULB) and the University of Oxford have come up with a theory that further challenges that standard view of causality as a linear progress from cause to effect.  In their new theoretical structure, cause and effect can sometimes take place in cycles, with the effect actually causing the cause.

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Fractal Pattern in a Quantum Material Confirmed for the First Time!

The word fractal has become increasingly popular, although the concept started more than two centuries ago in the 17th century with prominent and prolific mathematician and philosopher Gottfried Wilhelm Leibnitz. Leibnitz is believed to have addressed for the first time the notion of recursive self-similarity, and it wasn’t until 1960 that the concept was formally stabilized both theoretically and practically, through the mathematical development and computerized visualizations by Benoit Mandelbrot, who settled on the name “fractal”.

Fractals are defined mainly by three characteristics:

  1. Self-similarity: identical or very similar shapes and forms at all scales.
  2. Iteration: a recursive relationship limited only by computer capacity. With sufficiently high performance, the iterations could be infinite. This allows for very detailed shapes at every scale, that modify with respect to the first iteration, manifesting the original shape at some levels of iteration. Because of this, fractals may have emergent properties, which make them a suitable tool for complex systems.
  3. Fractal dimension, or fractional dimensions: describes the counter-intuitive notion that a measured length changes with the length of the measuring stick used; it quantifies how the number of scaled measuring sticks required to measure, for example, a coastline, changes with the scale applied to the stick.
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