brain complexity: probably calculating dendrites (Introduction)

by David Turell @, Wednesday, January 15, 2020, 00:26 (180 days ago) @ David Turell

Recent research points to this ability, which increases the brains computing power by many, many times:

https://www.quantamagazine.org/neural-dendrites-reveal-their-computational-power-20200114/

"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.

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"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.

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"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.

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"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.

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"...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.

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"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.

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"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.

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“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.'”

Comment: Forward looking and not established as yet, but very likely true. Not by chance.


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