

Evidence that dendrites actively process information in the brain - atpaino
http://www.kurzweilai.net/evidence-that-dendrites-actively-process-information-in-the-brain

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akavi
This is the very sort of interaction that leads me to be very, _very_
unoptimistic about ever seeing Moore's Law style runaway advancement in
biotechnology.

Biology, it seems, is deeply _unabstractable_. Ie, as one moves up the levels
of organization, one rarely (never?) reaches a point where a higher level can
be fully modeled without also fully modeling each of the lower levels.

This is in sharp contrast to computer engineering, where, for example, one can
model a processor with all practical accuracy by treating the individual as
idealized boolean logic (As we move towards smaller and smaller transistors,
this abstraction is threatening to become "leaky", but this has been true thus
far throughout the Moore-ian advancement).

I suspect that there may be a limit to the degree of complexity humans can
"manage", and thus, without the benefit of effective abstraction, there is a
limit on the degree of advancement we can achieve in bending biology to our
will.

(An example that speaks to this, in my mind, is the fact that our attempts to
chemically tweak our own biochemistry (viz. drugs) are hilariously crude
(flood the system with a handful of chemicals, which hopefully drives the
system as a whole in the general direction we want) compared to the regulation
that the body carries out on its own.)

~~~
daughart
Bioengineering and synthetic biology will never be like electrical
engineering, but some of the differences can be exploited.

For one, you can use directed evolution to optimize a biological system
without relying on rational design.

For another, biological development is massively flexible. Consider that when
you evolve a longer arm, you don't need to mutate genes to ensure you have
longer muscles, tendons, nerves, etc. In fact, you can grow an entirely new
arm by just initiating a limb bud at the correct time in development. By
contrast, in electrical engineering all design aspects are "rational" \- when
you change one part, you must change the other parts to compensate.

Modularity and reductionism are "problems" only in the sense that they reflect
differences between our engineering strategy and the substrate we are trying
to engineer. We must discover the engineering principles that match the
substrate.

~~~
PeterisP
As you say, even futuristic bioengineering will never be like electrical
engineering, but it does have parallels with software development.

It feels somewhat similar to declarative programming - our genes contain a
large bunch of code that, in effect, says 'if you're seeing chemical X (which
should mean that you're on the edge of a limb bud, then produce chemical
Y/grow differently/become a skin cell'. And a bug in some other, far-away code
can make an embryo grow, for example, a sixth toe, by invoking already
existing code that will connect it to your foot and add toenails.

And we have some idea on how to work with such code - sure, it's far away from
what we'd call well engineered or intelligently designed code, it's a big
horrible pile of buggy spaghetti code that mostly works in most conditions if
we discount the large portion of cases where the egg doesn't even develop into
a valid embryo. And there's 'bug parity' where fixing a single-item bug is
likely to create another bug elsewhere because it relied on the first part
being always buggy. And, of course, it's undocumented obfuscated 'assembly
code'. But the advantage is that it's only a singe codebase (although even
larger than healthcare.gov) with no 'completely new and different' releases
coming, so all of us together have to learn it once, and it is almost the same
codebase that we'd also use to alter our corn, cows, flu and mosquitoes.

------
bl
If you are interested in making a very stretched analogy, demonstrating
dendritic information processing is like realizing that a CPU's transistor is
actually itself a little CPU that is itself capable of quite sophisticated
computation. In fact, _most_ of a neuron's computation my be carried out by
the dendrites. Don't get tied up in the over-simplified model of
dendrite=antenna, soma=computer, axon=wires.

Active dendritic information processing has, for several decades, been
theorized and modeled. The combination of two-photon microscopy and more
"classical" electrophysiology techniques (like patch clamping used in this
article) is finally opening the theories to experimentation.

[Not to be too critical, but this paper is far from the first to
experimentally investigate dendritic information processing. I, personally, am
glad some segment of HN is interested in neural computation.]

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kylebrown
I like this connection between memristors and nuerons: " _From an information
processing perspective, this tutorial shows that synapses are locally-passive
memristors, and that neurons are made of locally-active memristors._ "[1]

1\.
[http://iopscience.iop.org/0957-4484/24/38/383001](http://iopscience.iop.org/0957-4484/24/38/383001)

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tannerc
Amazing to have some evidence of the processing capabilities dendrites could
possess. Though this only makes our understanding of the brain _that much_
slimmer.

With billions of neurons and dendrites interacting all the time, if each are
compartmentalized we're going to have a difficult time coming up with a model
to replicate the effects. Which, as I understand it, is our goal in an effort
to better understand how the brain works overall.

Still, with this insight it's clear we've got some immensely powerful hardware
bouncing around between our ears. What a truly brilliant machine.

~~~
PeterisP
I wouldn't be discouraged - this is actually a way of computation that we
could "read" by looking at the brain.

The dentritic 'computations' would depend on the geometry of the dendrite and
the location of synaptic connections; so the current projects that want to
slice a brain in thin slices, scan them, and reconstruct the neurons, would be
able to build an exact map for that type of computation, simply by
automatically converting each dendrite's connection geometry to a
formula/model of that dendritic tree.

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jasallen
Novice question:

A given dendrite has a voltage raise, presumable because of transmitter from a
neighboring neuron. That voltage increase will always be local unless it is
adequate (as it spread and dissipates on its way to the cell body) for an
action potential.

If they showed an action potential starting at the dendrite, then I would
expect it to eventually move to the rest of the cell body and then I wouldn't
expect the language about 'not seeing the rest of the cell light up'. So, how
did they measure/show actual processing? I'm missing that part.

~~~
bl
The voltage change (i.e., depolarization) is not strictly local.

In some cases, depending on the actual geometry of the dendrite and the
particular complement of voltage-activated ion channels, the voltage change as
a result of neurotransmitter release might lead to quite a distributed
depolarization even without triggering a dendritic action potential.

Conversely, an action potential initiated in the dendrites doesn't necessarily
faithfully propagate to the cell body (soma). This is also dependent on the
local geometry and ion channel distribution. Dendritic action potentials are
not all-or-nothing events like those of the axon.

To answer your question: Smith, _et al._ , did observe dendritic action
potentials (spikes) by measuring a proxy: calcium influx indicated by a
fluorescent dye that changes efficiency when bound to calcium. This calcium
influx, and by extension, the dendritic spike, is what was spatially-
restricted. The authors are extrapolating information processing from the
spatially-restricted dendritic spike.

~~~
jasallen
Thanks for the answer.

So just to close the loop and make sure I got it, a couple follow ups

'processing' in this case would refer to integrating
signals/voltages/neurotransmitters from more than one neighboring neuron?

How do they show that this was processing/integrating and not just particular
sensitivity to one external stimulus?

For 'processing' to be meaningful, would it not have to share the result? In
other words propagate the action potential or release neurotransmitter?

~~~
PeterisP
I'm not a biologist and the parent poster seems to know in far more detail,
but from a bunch of neuroscience lectures on how the dentritic spikes travel
up to the soma, my takeaway (as a computer guy) was 'hmmm, it looks like a
system implemented in FPGA layouts - the geometry features can work as logic
gates or delays'; and 'hmmmm, it looks I could design a dendritic tree
geometry for almost any boolean function of the inputs, so any computer-chip-
like-functionality could be built out of them'.

I mean, if I needed (A xor B) and (C or D), then my impression is a single
neuron with rather simple geometry and appropriate dendritic connections could
calculate that in the sense that this neuron would spike iff the A,B,C,D
neurons spiked as required by that formula; but since neurons tend to have
much much more connections, then each neuron is technically capable of much
more complex calculations, even if many of them in the end do something like
'spike iff any 100+ of my 1000 inputs are spiking'.

It's not so simple as that because timing is also relevant, and there were
examples of known dendritic structures that do "processing" in terms that a
neuron spikes if it receives A slightly before B, but doesn't spike if it
receives A slightly after B; so it can be used for detecting motion direction
and such.

~~~
bl
"[I]t looks I could design a dendritic tree geometry for almost any boolean
function of the inputs".

That's my outlook on the structure-function link between dendritic morphology
and dendritic information processing, with the modification that I'd not
restrict it to boolean functions. There are very many more types of functions,
linear and non-linear, that can conceivably be built out of neuronal
dendrites.

And I like the nuance of your second paragraph. There are all sorts of wacky,
complex calculations one can image being possible, but any one neuron may
implement a subset. Now, across a few hundred billion neurons in a mammalian
nervous system...

You're spot on with regard to timing, too. All this "information processing"
with branched dendrites + non-linear ion channels are greatly expanded with a
timing component.

~~~
PeterisP
Well, AFAIK you don't need anything more than boolean functions, since if
we're talking about single spikes (not spike frequency), then there either is
or isn't a spike, you don't get some spikes larger than other.

The linear/nondigital functions IMHO seem to be used as implementation details
- for example, a neuron "fire iff 1+ VIP-input fires or 3+ normal inputs fire"
can be implemented in wetware by having 'vip-inputs' have thrice as strong
synaptic connection, summing all input values in the dendrite, and adjusting
so that the firing threshold is appropriate (i.e. a linear function); but in
silicon the same thing can (should?) be implemented as a boolean function /
logic gates.

~~~
bl
I hope no one interpreted my statements to suggest that anything you said was
wrong. Just trying to fill in details.

I merely want to avoid prematurely narrowing the range of functions that are
possible. If we, for the moment, think of the neural computation of a single
neuron as a neural network, then the spike/no-spike decision would be in the
last layer and a whole host of linear/non-linear (some not necessarily
boolean) functions could be implemented by the dendrites. And some single
neuron processing we already know behaves in a non-boolean manner.

Be aware, just because arbitrarily powerful logic could be constructed solely
out of boolean components (I don't even know if this is true. Isn't this kinda
what is going on in an FPGA?) doesn't mean that neural hardware is purposed
the same way. They may very well may be analog, at least for some
computations.

And to speak to your second paragraph, I should declare my personal biases. As
a dendritic physiologist, I wasn't much interested in whole-cell firing
characteristics, but in the dendrite's sub-threshold behavior.

 _How do a smattering of synaptic inputs, each with varying strengths,
interact within the complex electrophysiological scaffolding provided by a
branched dendrite layered with non-uniform, non-linear ion channel
distributions?_

So my perspective is somewhat inverted: To me, neuron firing is the
implementation detail! <smilie face>

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codeulike
Reminds me of Roger Penrose's assertion in Shadows of the Mind that the
microtubules within the neurons might be doing the work - making each Neuron
into a metaphorical computer with millions of transistors. This is a different
idea but the same conclusion - Neurons aren't the lowest level of
computational structure in the brain, which means we have been underestimating
the complexity and power of the brain by many orders of magnitude.

~~~
OvidNaso
And this is, somewhat ironically, very bad news for Mr. Kurzweil.

~~~
atpaino
Actually, this may be very good news for Kurzweil. In his last book "How to
Create a Mind", he lays out a theory centered around a "pattern-recognizer"
unit that is repeated throughout the columns and regions of the neocortex. In
his book, he assumed it to be made up of several neurons wired in a specific
manner, but if each neuron can do some hierarchical processing of its own then
the pattern-recognizer might be reducible to a single neuron.

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DigitalJack
It's very interesting research, but I have to say I'd have a hard time being
clinically detached with regards to probing a live mouse and working with it,
knowing I was going to kill it when my testing was done.

~~~
invalidOrTaken
My cousin does biomedical research. She said it gets much, much easier.

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daughart
I hope people in the connectome camp take this to heart. I strongly doubt that
modeling the connections of neurons will reveal the way the brain works. The
mouse and rat brains are very similar in connectivity, but the behavior of the
mouse and rat are quite different. One explanation is that the individual
neurons are actually processing information differently, and so differences
arise out of neuron functionality rather than connectivity. This research
bolsters the argument that meaningful information processing occurs within
individual neurons, and even at the sub-cellular level.

~~~
PeterisP
If this is the right way, then exactly the connectome camp will reveal the way
the brain works - all their methodologies on how to extract connectomes from
brain samples also (by necessity) reconstruct the whole dendritic tree
structure through which the synapse is linked; so all parameters for these
dentritic computations would also be included in their data.

