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Comment by nynx | Hacker News Reader

You can keep adding compartments and solving Hodgkin-Huxley at small time-steps to get basically arbitrary resolution. It definitely gets a lot of expensive than simpler models (and there’s some evidence that simpler models will converge to the same result).

The issue is that we don’t know if our neuron models are complete. Massive simulations are the easy part—computers are very, very fast and large these days.

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They are also "discrete", which kind of gets in the way of modeling continuous processes in this case. And not very good about modeling trillions of things at the same time due to communication overhead (both latency _and_ energy). And all of that is the "easy" part. The hard part is we don't even know what the system needs to look like.

Yann LeCun said decades ago that in spite of all the progress there won't be anything artificial that's as smart as a rat in his lifetime. In 2021 that bet is still a safe one.

The continuous vs. discrete thing is largely moot in my opinion. The low-significant bits of any model of a physical process are going to be noise anyhow. You can model more accurately if there's some data that's missing, but the fact that the real world is continuous (ignoring that it's really not) doesn't really matter.

We can model many billions of things at once. Take a look at the spinnaker system. Sure, we're not at the level of simulating 100 billion biologically plausible neurons yet, but we can do a few billion and the techniques are likely to scale well.

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