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Anyway, cells can hold (super) primitive models of the world and maintain internal balance in the face of anticipated events. I've occasionally run into scienc

Anyway, cells can hold (super) primitive models of the world and maintain internal balance in the face of anticipated events. I've occasionally run into science podcasts, going back almost a decade, where some researcher talks about the computational power of cell membranes, and how the synapses evolved from these mechanisms. Amoebas and paramecia navigate their environments, sense, and react through their cell membranes. Apparently, synapses evolved from these mechanisms. The upshot of this for AI, is that the neural network model may be drastically incomplete, with far more computation actually happening inside actual individual neurons.

PeterisP 3y ago
Nobody is attempting to have one-to-one correspondence between neurons and artificial "neurons", the fact that a single biological does much more doesn't imply some limitation or incompleteness (as long as the same computations can be implemented simply by having more of them, and as far as we understand, that seems to be the case) - the choice is primary because due to how our hardware parallelization works, we'd prefer to implement the exact same behavior with 1000 structurally identical simple "neurons" rather than have a single more complex "emulated neuron" that requires more complicated logic that can't be straightforwardly reduced to massive matrix multiplication.
- stcredzero 3y ago
  that requires more complicated logic that can't be straightforwardly reduced to massive matrix multiplication What are the rationale for thinking it doesn't matter? Or is it the case that researchers think a larger number of simpler neurons could be equivalent to a smaller number of "complex emulated neurons?"
  - PeterisP 3y ago
    Yes, computation as such is sufficiently universal that it's very reasonable to assume that a larger number of simpler neurons would be equivalent to a smaller number of "complex emulated neurons".

PeterisP 3y ago

Nobody is attempting to have one-to-one correspondence between neurons and artificial "neurons", the fact that a single biological does much more doesn't imply some limitation or incompleteness (as long as the same computations can be implemented simply by having more of them, and as far as we understand, that seems to be the case) - the choice is primary because due to how our hardware parallelization works, we'd prefer to implement the exact same behavior with 1000 structurally identical simple "neurons" rather than have a single more complex "emulated neuron" that requires more complicated logic that can't be straightforwardly reduced to massive matrix multiplication.

stcredzero 3y ago
that requires more complicated logic that can't be straightforwardly reduced to massive matrix multiplication What are the rationale for thinking it doesn't matter? Or is it the case that researchers think a larger number of simpler neurons could be equivalent to a smaller number of "complex emulated neurons?"
- PeterisP 3y ago
  Yes, computation as such is sufficiently universal that it's very reasonable to assume that a larger number of simpler neurons would be equivalent to a smaller number of "complex emulated neurons".