[jimfleming]

> That needs to work before moving to more complexity.

It really depends on what level of abstraction you care to simulate. OpenWorm is working at the physics and cellular level, far below the concept level as in most deep learning research looking to apply neuroscience discoveries, for example. It’s likely easier to get the concepts of a functional nematode model working or a functional model of memory, attention, or consciousness than a full cellular model of these.

More specifically, a thousand cells sounds small in comparison to a thousand layer ResNet with millions of functional units but the mechanics of those cells are significantly more complex than a ReLU unit. Yet the simple ReLU units are functionally very useful and can do much more complex things that we still can’t simulate with spiking neurons.

The concepts of receptive fields, cortical columns, local inhibition, winner-take-all, functional modules and how they communicate / are organized may all be relevant and applicable learnings from mapping an organism even if we can’t fully simulate every detail.

[Reelin]

The trouble is that (assuming sufficient computational power) if we can't simulate it then we don't really understand it. It's one thing to say "that's computationally intractable", but entirely another to say "for some reason our computationally tractable model doesn't work, and we don't know why".

Present day ANNs may well be inspired by biological systems but (as you noted) they're not even remotely similar in practice. The reality is that for a biological system the wiring diagram is just the tip of the iceberg - there's lots of other significant chemical things going on under the hood.

I don't mean to detract from the usefulness of present day ML, just to agree with and elaborate on the original point that was raised (ie that "we have a neural wiring diagram" doesn't actually mean that we have a complete schematic).

[jimfleming]

I'm aware of that and I've done quite a bit of work on both spiking neural networks and modern deep learning. My point is that those complexities are not required to implement many important functional aspects of the brain: most basically "learning" and more specifically, attention, memory, etc. Consciousness may fall into the list of things we can get functional without all of the incidental complexities that evolution brought along the way. It may also critically depend on complexities like multi-channel chemical receptors but since we don't know we can't say either way.

It's a tired analogy but we can understand quite a lot about flight and even build a plane without first birthing a bird.

[staticassertion]

But we understand tons of things without simulating them.

[jes5199]

I recently saw this video of living neurons: https://www.youtube.com/watch?v=2TIK9oXc5Wo (I don't actually know the original source of this)

and just looking at the way they dance around - they're in motion, they're changing their connections, they're changing shape - is so entirely unlike the software idea of a neural network that it makes me really doubt that we're even remotely on the right track with AI research

[jacobush]

Amazing video! And these poor neurons are squished between glass, imagine them crawling around in a 3D space.

[Animats]

It really depends on what level of abstraction you care to simulate.

The article starts out "At the Allen Institute for Brain Science in Seattle, a large-scale effort is underway to understand how the 86 billion neurons in the human brain are connected. The aim is to produce a map of all the connections: the connectome. Scientists at the Institute are now reconstructing one cubic millimeter of a mouse brain, the most complex ever reconstructed."

So the article is about starting with the wiring diagram and working up. My point is that, even where we already have the wiring diagram for an biological neural system, simulating what it does is just barely starting to work.

[__s]

A good comparison exists with emulators, where transistor level emulation is ill advised for most hardware