6 ms·

Evidence that dendrites actively process information in the brain

88 points by atpaino 13y ago

tannerc 13y ago
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 13y ago
  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.
bl 13y ago
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.]
kylebrown 13y ago
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
codeulike 13y ago
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 13y ago
  And this is, somewhat ironically, very bad news for Mr. Kurzweil.
  - daughart 13y ago
    I think it's actually quite promising. We're good at cell biology but poor at systems biology. If we can convert neuroscience to understanding what types of neurons exist and how they function, that is probably very tractable. In comparison, even measuring the connectome is an insane problem, and modeling trillions of neurons to reveal brain function seems intractable.
  - atpaino 13y ago
    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.
- PeterisP 13y ago
  We've always known that we need to model the 10^14 synapses (and their strengths) that connect neurons. The dendrite trees that connect these synapses to a soma have at most another 10^14 branching points, so modeling them all explicitly, in the worst case, only doubles the model size; but it might also give also significant possibilities for optimization, if these 'dendritic' calculations can be modelled as a simple formula.
jasallen 13y ago
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 13y ago
  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 13y ago
    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?
    - 13y ago
DigitalJack 13y ago
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 13y ago
  My cousin does biomedical research. She said it gets much, much easier.
- nooneelse 13y ago
  In my experience/opinion, the worst part is when you try and try and get no good data at all from one.
akavi 13y ago
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.)
- hyperion2010 13y ago
  I wouldn't be quite so pessimistic. We can capture a large part of neuronal variability using Hodgkin Huxley type models (like the one they use in the paper). Dendritic spikes have been hypothesized to be involved in computations for quite a while we just haven't had evidence in vivo. My take home from the paper is that the voltage dependent active properties of the dendritic tree act as an amplifier for synaptic events. This doesn't fundamentally change how we think neurons work, it just fills in one of the major gaps in our understanding of how relatively few synaptic events could lead to a somatic action potential--something that is very hard to explain if dendrites only passively integrate incoming synaptic events. To give an example, there are connections in the brain between excitatory neurons and inhibitory neurons that are known to basically be 1:1 with virtually no failure rate, one spike in the excitatory neuron will evoke a spike in the inhibitory neuron pretty much every single time. Based on what we know about synaptic failure rates and the total number of synaptic events we think are required to generate and action potential, this phenomena is difficult to explain. Active dendritic properties as described in the paper provide a possible mechanism. edit: I should say that dendritic spikes probably act as a kind of conditional amplifier for synaptic events. The conditions that come to mind are spatial and temporal proximity. This does complicate the idea that relief of the NMDA Mg2+ block is used to detect coincidence of somatic action potentials with presynaptic glutamate release, suggesting that the Mg2+ block may also be used to detect coincidence of a single synaptic event with other nearby synaptic events.
daughart 13y ago
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 13y ago
  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.
losethos 13y ago
doctor-nigger want to sue for a trillion dollars.

88 points by atpaino 13y ago

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 13y ago
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.

bl 13y ago

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.]

kylebrown 13y ago

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

codeulike 13y ago

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 13y ago
And this is, somewhat ironically, very bad news for Mr. Kurzweil.
- daughart 13y ago
  I think it's actually quite promising. We're good at cell biology but poor at systems biology. If we can convert neuroscience to understanding what types of neurons exist and how they function, that is probably very tractable. In comparison, even measuring the connectome is an insane problem, and modeling trillions of neurons to reveal brain function seems intractable.
- atpaino 13y ago
  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.
PeterisP 13y ago
We've always known that we need to model the 10^14 synapses (and their strengths) that connect neurons. The dendrite trees that connect these synapses to a soma have at most another 10^14 branching points, so modeling them all explicitly, in the worst case, only doubles the model size; but it might also give also significant possibilities for optimization, if these 'dendritic' calculations can be modelled as a simple formula.

jasallen 13y ago

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 13y ago
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 13y ago
  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?
  - 13y ago

DigitalJack 13y ago

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 13y ago
My cousin does biomedical research. She said it gets much, much easier.
nooneelse 13y ago
In my experience/opinion, the worst part is when you try and try and get no good data at all from one.

akavi 13y ago

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.)

hyperion2010 13y ago
I wouldn't be quite so pessimistic. We can capture a large part of neuronal variability using Hodgkin Huxley type models (like the one they use in the paper). Dendritic spikes have been hypothesized to be involved in computations for quite a while we just haven't had evidence in vivo. My take home from the paper is that the voltage dependent active properties of the dendritic tree act as an amplifier for synaptic events. This doesn't fundamentally change how we think neurons work, it just fills in one of the major gaps in our understanding of how relatively few synaptic events could lead to a somatic action potential--something that is very hard to explain if dendrites only passively integrate incoming synaptic events. To give an example, there are connections in the brain between excitatory neurons and inhibitory neurons that are known to basically be 1:1 with virtually no failure rate, one spike in the excitatory neuron will evoke a spike in the inhibitory neuron pretty much every single time. Based on what we know about synaptic failure rates and the total number of synaptic events we think are required to generate and action potential, this phenomena is difficult to explain. Active dendritic properties as described in the paper provide a possible mechanism. edit: I should say that dendritic spikes probably act as a kind of conditional amplifier for synaptic events. The conditions that come to mind are spatial and temporal proximity. This does complicate the idea that relief of the NMDA Mg2+ block is used to detect coincidence of somatic action potentials with presynaptic glutamate release, suggesting that the Mg2+ block may also be used to detect coincidence of a single synaptic event with other nearby synaptic events.

daughart 13y ago

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 13y ago
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.

losethos 13y ago

doctor-nigger want to sue for a trillion dollars.