3 ms·

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. Biol

by 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
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 13y ago
  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.
atpaino 13y ago
Biology may be very "unabstractable", but that doesn't mean we can't still learn enough about its high level behavior to create practical systems in its image. For example, it is unlikely that whatever structure or property of the brain makes it intelligent exists only at the molecular level, so we likely won't need to simulate the brain at that low of a level to create an intelligent machine.
groovy2shoes 13y ago
The problem is that our knowledge is limited. Once we have a better understanding of certain biological processes, we may be able to find the right abstractions.
- jostmey 13y ago
  You might be missing the point. Biological systems are the result of Natural selection, and the processes of Natural selection are fundamentally irrational. Any biological system, including our own brain, is essentially the outcome of a random, irrational process. There may be cases where we can arrive at some abstractions, but such cases may be the exception, not the norm.
  - pkroll 13y ago
    In that irrational assumes sentience, OK. The randomness being "we can't possibly predict all the elements that went into X happening" OK. But neither of those matters in making an abstraction: the biological components didn't evolve outside of chemistry and physics. We can argue about how much detail would be needed, but is there really anything we can't abstract?
floobynewb 13y ago
I suppose the question is: Are there appropriate abstractions which would bring conceptual simplicity to these systems? I find it hard to believe that there is not. A system which does not have such a property is more difficult for natural selection to operate upon. The changes which are likely to happen to a lineage over the course of time are likely to have evolved to be likely to move the lineage closer to a locally optimal phenotype. I do not think that a system which is incompressible, would display such dynamics, the system would be chaotic and small changes in the genotype would lead to divergent phenotypes wrt the fitness landscape. Perhaps the appropriate abstractions are spread out both temporaly and spatially.

by akavi 13y ago

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

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 13y ago
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.

atpaino 13y ago

Biology may be very "unabstractable", but that doesn't mean we can't still learn enough about its high level behavior to create practical systems in its image. For example, it is unlikely that whatever structure or property of the brain makes it intelligent exists only at the molecular level, so we likely won't need to simulate the brain at that low of a level to create an intelligent machine.

groovy2shoes 13y ago

The problem is that our knowledge is limited. Once we have a better understanding of certain biological processes, we may be able to find the right abstractions.

jostmey 13y ago
You might be missing the point. Biological systems are the result of Natural selection, and the processes of Natural selection are fundamentally irrational. Any biological system, including our own brain, is essentially the outcome of a random, irrational process. There may be cases where we can arrive at some abstractions, but such cases may be the exception, not the norm.
- pkroll 13y ago
  In that irrational assumes sentience, OK. The randomness being "we can't possibly predict all the elements that went into X happening" OK. But neither of those matters in making an abstraction: the biological components didn't evolve outside of chemistry and physics. We can argue about how much detail would be needed, but is there really anything we can't abstract?

floobynewb 13y ago

I suppose the question is: Are there appropriate abstractions which would bring conceptual simplicity to these systems? I find it hard to believe that there is not. A system which does not have such a property is more difficult for natural selection to operate upon. The changes which are likely to happen to a lineage over the course of time are likely to have evolved to be likely to move the lineage closer to a locally optimal phenotype. I do not think that a system which is incompressible, would display such dynamics, the system would be chaotic and small changes in the genotype would lead to divergent phenotypes wrt the fitness landscape. Perhaps the appropriate abstractions are spread out both temporaly and spatially.