3 ms·

You forgot to mention, crucially, that neurons in close proximity affect each other, which is just one of the things that makes modeling of more than a few neur

by m0zg 7y ago

You forgot to mention, crucially, that neurons in close proximity affect each other, which is just one of the things that makes modeling of more than a few neurons in time domain a complete non-starter. It all results in enormous systems of PDEs which we don't know how to solve yet at all. You could say that we do not have the right mathematical apparatus to model any such thing.

McTossOut 7y ago
Pure physical modeling are likely a bad representation for the phenomena resulting in intelligence, especially granted we've simulated it with much simpler discrete structures. PDEs may even be disastrously bad, like trying to describe a line in space with a table of points, instead of the degrees of freedom. I would imagine that a PDE may cover diffuse behaviors governing say, how learning happens mechanically, but it is almost certainly a language/representational barrier, the relationship between the structure of the animal mind, learning, and seemingly simple phenomena, like afterimages. The molecules are arbitrary and the timescale doesn't matter.
TaupeRanger 7y ago
I don't follow that. What would prevent (perhaps quite slow) simulation of a larger system of such neurons? E.g. N-body problems are analytically beyond us, but can be simulated to arbitrary precision with certain trade-offs.
- m0zg 7y ago
  Time domain solutions do not exist for more than a dozen neurons. At least they did not when I took a computational neuroscience MOOC a couple of years ago. State of the art at the time was the nervous system of an earthworm. That is, if you consider what you actually need to do to simulate how potentials will change in the brain over time give a certain starting state and stimuli, the math gets so complicated (and awkward) so quickly that it's not really tractable with the mathematical (or simulation) apparatus we currently have to go beyond such trivial systems.

by m0zg 7y ago

McTossOut 7y ago

Pure physical modeling are likely a bad representation for the phenomena resulting in intelligence, especially granted we've simulated it with much simpler discrete structures. PDEs may even be disastrously bad, like trying to describe a line in space with a table of points, instead of the degrees of freedom. I would imagine that a PDE may cover diffuse behaviors governing say, how learning happens mechanically, but it is almost certainly a language/representational barrier, the relationship between the structure of the animal mind, learning, and seemingly simple phenomena, like afterimages. The molecules are arbitrary and the timescale doesn't matter.

TaupeRanger 7y ago

I don't follow that. What would prevent (perhaps quite slow) simulation of a larger system of such neurons? E.g. N-body problems are analytically beyond us, but can be simulated to arbitrary precision with certain trade-offs.

m0zg 7y ago
Time domain solutions do not exist for more than a dozen neurons. At least they did not when I took a computational neuroscience MOOC a couple of years ago. State of the art at the time was the nervous system of an earthworm. That is, if you consider what you actually need to do to simulate how potentials will change in the brain over time give a certain starting state and stimuli, the math gets so complicated (and awkward) so quickly that it's not really tractable with the mathematical (or simulation) apparatus we currently have to go beyond such trivial systems.