6 ms·

> electrical signals weaken more as they flow along human dendrites, resulting in a higher degree of electrical compartmentalization, meaning that small section

by hliyan 8y ago

> electrical signals weaken more as they flow along human dendrites, resulting in a higher degree of electrical compartmentalization, meaning that small sections of dendrites can behave independently from the rest of the neuron Reading this, I just realized that all this time I was using the wrong computing analogy to understand the brain: neurons are not the transistors of the brain, dendrites are!

teekert 8y ago
To be honest, using a transistors analogy may keep you on the wrong path for a long time as well :) There is not a lot of "digital" going on in those cells...
- JoeAltmaier 8y ago
  Wait - transistors are not digital they're analog. And neurons are terribly digital, acting as rate controllers and pulse counters and dividers and so on.
  - hliyan 8y ago
    True. However, logic gates constructed from transistors are digital. I'm wondering what the corresponding higher level neuronal structure would be...
    - JoeAltmaier 8y ago
      Neurons are already logic gates. And FFT devices, and discriminators and ...
      - TeMPOraL 8y ago
        So basically neurons are ASICs, partially digital and partially analog.
        JackFr 8y ago
        They're basically cogs, rods and gears. Seriously, molecular biologists devote decades of study and publish volumes figuring out how some small virus 'works'. A human neuron is orders of magnitude more complex than such a virus. To dismiss it as 'like an _____', is a (sometimes necessary) analogy of very little explanatory depth.
        JoeAltmaier 8y ago
        Metaphors help understanding. A neuron is more on the order of an entire logic subsystem, than a transistor or a gate. That's a reasonable comparison-of-complexity statement.
        TeMPOraL 8y ago
        I'm not dismissing things. In fact, 'JoeAltmaier is correct that my analogy here was in context of the usual "neuron as signal adder" / "neuron as transistor". And yes, I'm aware neurons are complex. But that does not mean all of that complexity is relevant to the task of thinking; all living cells have machinery related to self-replication, self-maintenance and survival in a biological system. Or to use another analogy - a smartphone is orders of magnitude more complicated than a flashlight. Yet in context of people illuminating their way during the night, a smartphone is just a flashlight.
    - davisr 8y ago
      Memistor/Memristor
      - Balgair 8y ago
        https://en.wikipedia.org/wiki/Memristor https://en.wikipedia.org/wiki/Memristor Many synapses behave very much like memristors, though caveats apply.
  - achileas 8y ago
    That's oversimplified and usually how it's taught at the undergrad level. Neurons and especially the networks they form are more analogous to analog electronics, but both analogies end up failing at some point.
    - protonfish 8y ago
      From what I know, neurons typically click on or off, so they are digital in that way. Signal strength is implied by the frequency of those clicks, so that's analog. Transistors are analog electronics, but used to make digital switches so it's sort of the reverse. I guess my point is that calling neurons analog or digital is not a very good analogy and should probably be avoided.
      - nickpsecurity 8y ago
        It's a mixed-signal, general-purpose ASIC done in wetware. Combines both digital and analog traits. Some of most interesting research I saw in hardware CompSci does the same. Never goes mainstream since industry prefers things that are easy to automate and scale up for obvious reasons.
      - ItsMe000001 8y ago
        But whether or not the incoming signals ever lead to a new action potential depends on so many factors (spacial, temporal, chemical, biological,... and it's all dynamic on many levels) that can all be called "analog" and are quite diverse that it's hard to call anything "digital". One action potential, that thing that brings to mind the "digital" analogy, really is a very small part in that system.
      - kazinator 8y ago
        Transistors also shut on and off fully in, say, a switched mode power supply, or a class D amplifier, or any other pulse-width-modulation-like scenario. That is all considered analog.
    - reubenswartz 8y ago
      IAMNANS (I am not a neuroscientist) but my very simple understanding is that to a reasonable approximation, inputs are analog (there’s a lot of nuance in if and how much signal makes it into a neuron) and outputs are digital (neuron fires or doesn’t), leading to all kinds of interesting circuitry.
  - robotrout 8y ago
    Any digital analogy fails, since the biological signals attenuate with distance from source. A digital gate acts as a repeater, re-boosting the signal strength to original level. That is not the case with neurons/dendrites. Perhaps a better model would be for each neuron to be modeled as a current source instead of a voltage source. The more parallel paths that current is funneled into, the smaller it gets, until it is below the threshold of detection. You can have your transistor model allowing/blocking that current, but never boosting it back up.
    - ItsMe000001 8y ago
      > the biological signals attenuate with distance from source The voltage signal is propagated in axons sometimes over very long distances (a meter, e.g. from the foot to the spine). It is renewed (see "Nodes of Ranvier") along the path for as long as needed, at distances that depend on how, or if, the axon is myelinated. That's why the speed is so slow (ca. 120 m/s max., often much slower) - it's actual ion movement through lots and lots of opening and closing voltage triggered channels all along the axons, compared to a purely electrical signal like in a metal wire. This means while in a wire the electrical field is from start to end, leading to electrons exiting on the far end pretty much instantaneously, in an axon am electrical field is only local, and only strong enough to trigger anything for about 1-2 millimeters at best. Then there have to be a new set of channels triggered by the electrical field further up the axon that renew the signal by letting in ions. Attenuation is more important in dendrites and neuron bodies, spatial distance of simultaneously incoming action potentials (via connected axons from other neurons) plays a role in determining whether a threshold is reached that would trigger firing an action potential from this neuron. Since a dendritic arbor can be quite extensive and have lots of branches location - where exactly along the branches does an incoming signal attach - matters a lot.
buboard 8y ago
It is and it isn't. There is a two-layer model of neuron that captures some properties like firing rates of hippocampal neurons, but there are also alternate models. E.g: https://static1.squarespace.com/static/5267aed6e4b03cb52f5e0a7b/t/52704e46e4b09a993f7fa02c/1383091782768/BrancoH%C3%A4usser_CON2010.pdf https://static1.squarespace.com/static/5267aed6e4b03cb52f5e0...
- hliyan 8y ago
  Thanks for this! I'm not in the neuroscience field, but the title itself tells me that I've a misconception that needs correcting.
michrassena 8y ago
Does this imply that the neuron is more of a power supply, and the properties of sections of dendrites (not the whole dendrite) and their connections is what does the actual computation? If so, that adds a layer of messy complexity which is fairly typical in living things.
- achileas 8y ago
  Both. Because neuroscience is hilariously complex and a big "fuck you" to neuroscientists. Computation happens at many levels - among dendrites, among axons (some neurons have multiple axons as well!), axonal branches, within the neuron, and also among various networks that arise (behavioral and structural networks). It's all a bit much, if you ask me.
protonfish 8y ago
Dendrites are just a specific part of a neuron (1), so you are no more correct. More importantly, neurons are nothing like transistors. Each neuron contains significant computing power. For example, in the retina of a frog there is a neuron that accepts as inputs dendrites from an area of light-sensitive cells (rods and cones) and only fires when a small, round, moving object is in the field of view. These neurons are colloquially called "bug perceivers." (2) 1 https://en.wikipedia.org/wiki/Neuron https://en.wikipedia.org/wiki/Neuron 2 "What the Frog's Eye Tells the Frog's Brain" (1959)

by hliyan 8y ago

teekert 8y ago

To be honest, using a transistors analogy may keep you on the wrong path for a long time as well :) There is not a lot of "digital" going on in those cells...

JoeAltmaier 8y ago
Wait - transistors are not digital they're analog. And neurons are terribly digital, acting as rate controllers and pulse counters and dividers and so on.
- hliyan 8y ago
  True. However, logic gates constructed from transistors are digital. I'm wondering what the corresponding higher level neuronal structure would be...
  - JoeAltmaier 8y ago
    Neurons are already logic gates. And FFT devices, and discriminators and ...
    - TeMPOraL 8y ago
      So basically neurons are ASICs, partially digital and partially analog.
      - JackFr 8y ago
        They're basically cogs, rods and gears. Seriously, molecular biologists devote decades of study and publish volumes figuring out how some small virus 'works'. A human neuron is orders of magnitude more complex than such a virus. To dismiss it as 'like an _____', is a (sometimes necessary) analogy of very little explanatory depth.
        JoeAltmaier 8y ago
        Metaphors help understanding. A neuron is more on the order of an entire logic subsystem, than a transistor or a gate. That's a reasonable comparison-of-complexity statement.
        TeMPOraL 8y ago
        I'm not dismissing things. In fact, 'JoeAltmaier is correct that my analogy here was in context of the usual "neuron as signal adder" / "neuron as transistor". And yes, I'm aware neurons are complex. But that does not mean all of that complexity is relevant to the task of thinking; all living cells have machinery related to self-replication, self-maintenance and survival in a biological system. Or to use another analogy - a smartphone is orders of magnitude more complicated than a flashlight. Yet in context of people illuminating their way during the night, a smartphone is just a flashlight.
  - davisr 8y ago
    Memistor/Memristor
    - Balgair 8y ago
      https://en.wikipedia.org/wiki/Memristor https://en.wikipedia.org/wiki/Memristor Many synapses behave very much like memristors, though caveats apply.
- achileas 8y ago
  That's oversimplified and usually how it's taught at the undergrad level. Neurons and especially the networks they form are more analogous to analog electronics, but both analogies end up failing at some point.
  - protonfish 8y ago
    From what I know, neurons typically click on or off, so they are digital in that way. Signal strength is implied by the frequency of those clicks, so that's analog. Transistors are analog electronics, but used to make digital switches so it's sort of the reverse. I guess my point is that calling neurons analog or digital is not a very good analogy and should probably be avoided.
    - nickpsecurity 8y ago
      It's a mixed-signal, general-purpose ASIC done in wetware. Combines both digital and analog traits. Some of most interesting research I saw in hardware CompSci does the same. Never goes mainstream since industry prefers things that are easy to automate and scale up for obvious reasons.
    - ItsMe000001 8y ago
      But whether or not the incoming signals ever lead to a new action potential depends on so many factors (spacial, temporal, chemical, biological,... and it's all dynamic on many levels) that can all be called "analog" and are quite diverse that it's hard to call anything "digital". One action potential, that thing that brings to mind the "digital" analogy, really is a very small part in that system.
    - kazinator 8y ago
      Transistors also shut on and off fully in, say, a switched mode power supply, or a class D amplifier, or any other pulse-width-modulation-like scenario. That is all considered analog.
  - reubenswartz 8y ago
    IAMNANS (I am not a neuroscientist) but my very simple understanding is that to a reasonable approximation, inputs are analog (there’s a lot of nuance in if and how much signal makes it into a neuron) and outputs are digital (neuron fires or doesn’t), leading to all kinds of interesting circuitry.
- robotrout 8y ago
  Any digital analogy fails, since the biological signals attenuate with distance from source. A digital gate acts as a repeater, re-boosting the signal strength to original level. That is not the case with neurons/dendrites. Perhaps a better model would be for each neuron to be modeled as a current source instead of a voltage source. The more parallel paths that current is funneled into, the smaller it gets, until it is below the threshold of detection. You can have your transistor model allowing/blocking that current, but never boosting it back up.
  - ItsMe000001 8y ago
    > the biological signals attenuate with distance from source The voltage signal is propagated in axons sometimes over very long distances (a meter, e.g. from the foot to the spine). It is renewed (see "Nodes of Ranvier") along the path for as long as needed, at distances that depend on how, or if, the axon is myelinated. That's why the speed is so slow (ca. 120 m/s max., often much slower) - it's actual ion movement through lots and lots of opening and closing voltage triggered channels all along the axons, compared to a purely electrical signal like in a metal wire. This means while in a wire the electrical field is from start to end, leading to electrons exiting on the far end pretty much instantaneously, in an axon am electrical field is only local, and only strong enough to trigger anything for about 1-2 millimeters at best. Then there have to be a new set of channels triggered by the electrical field further up the axon that renew the signal by letting in ions. Attenuation is more important in dendrites and neuron bodies, spatial distance of simultaneously incoming action potentials (via connected axons from other neurons) plays a role in determining whether a threshold is reached that would trigger firing an action potential from this neuron. Since a dendritic arbor can be quite extensive and have lots of branches location - where exactly along the branches does an incoming signal attach - matters a lot.

buboard 8y ago

It is and it isn't. There is a two-layer model of neuron that captures some properties like firing rates of hippocampal neurons, but there are also alternate models. E.g: https://static1.squarespace.com/static/5267aed6e4b03cb52f5e0a7b/t/52704e46e4b09a993f7fa02c/1383091782768/BrancoH%C3%A4usser_CON2010.pdf https://static1.squarespace.com/static/5267aed6e4b03cb52f5e0...

hliyan 8y ago
Thanks for this! I'm not in the neuroscience field, but the title itself tells me that I've a misconception that needs correcting.

michrassena 8y ago

Does this imply that the neuron is more of a power supply, and the properties of sections of dendrites (not the whole dendrite) and their connections is what does the actual computation? If so, that adds a layer of messy complexity which is fairly typical in living things.

achileas 8y ago
Both. Because neuroscience is hilariously complex and a big "fuck you" to neuroscientists. Computation happens at many levels - among dendrites, among axons (some neurons have multiple axons as well!), axonal branches, within the neuron, and also among various networks that arise (behavioral and structural networks). It's all a bit much, if you ask me.

protonfish 8y ago

Dendrites are just a specific part of a neuron (1), so you are no more correct. More importantly, neurons are nothing like transistors. Each neuron contains significant computing power. For example, in the retina of a frog there is a neuron that accepts as inputs dendrites from an area of light-sensitive cells (rods and cones) and only fires when a small, round, moving object is in the field of view. These neurons are colloquially called "bug perceivers." (2) 1 https://en.wikipedia.org/wiki/Neuron https://en.wikipedia.org/wiki/Neuron 2 "What the Frog's Eye Tells the Frog's Brain" (1959)