
A Complex Hierarchy of Avoidance Behaviors in a Single-Cell Eukaryote - bookofjoe
https://www.cell.com/current-biology/fulltext/S0960-9822(19)31431-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0960982219314319%3Fshowall%3Dtrue
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fiftyfifty
This kind of behavior in single celled organisms leads me to believe that
there is a fair amount of complex logic handled by individual neurons in the
brain, that is currently not being modeled in the state of the art neural
networks. The bottom line, I guess is our understanding of biological
intelligence has a long way to go and correspondingly AI also has a long way
to go before it can hope to simulate intelligence. It also means we may need a
lot more computing power to simulate a complex intelligence if each neuron has
its own complex set of behaviors that need to be simulated.

~~~
solipsism
_This kind of behavior in single celled organisms leads me to believe that
there is a fair amount of complex logic handled by individual neurons in the
brain, that is currently not being modeled in the state of the art neural
networks_

A highschool biology textbook could have told you the same. Each neuron is an
incredibly complex systems of pumps, channels, and pathways. There are many
kinds of inhibition and excitation, both chemical and electrical. There's
reuptake, active gene regulation causing dynamic protein synthesis, etc etc
etc. Individual neurons can be multiple feet long.

Every cell in your body is much, much more complex than we could ever imagine.

 _our understanding of biological intelligence has a long way to go and
correspondingly AI also has a long way to go before it can hope to simulate
intelligence._

That seems to assume mimicking the specifics of biology is necessary for
progress. It's an open question, but that idea seems a bit silly to me.

~~~
mlyle
The hard part is that we don't know the order of the problem.

We know a neuron and synapse count for humans, but we don't know if that's
minimal for intelligence.

We don't know how complicated of a model of a neuron we need to get by with an
equivalent number of neurons/synapses. Or what the tradeoff is-- how many more
neurons we need for a simpler model, if that's even possible. Can we run
smaller numbers of neurons to have a "slow" AGI? Does the fact that computers
can operate the connections and weightings quicker earn us anything?

And then you have the people like Penrose who argue there must be some quantum
magic somehow being used by the neurons to perform quantum consciousness
computation stuff.

I mean, I suspect what you imply is right: we can get by with something _much
less_ than an exhaustive model of the human brain. But I can't make a rigorous
argument for it, let alone _how much less_.

P.S. There's a certain irony to be replying to someone named "solipsism"
advancing this argument ;)

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dr_dshiv
Individual Human neurons can have over 4 meters of motile dendrites (they are
more like amoeba pseudopods than tree branches), with over 2 million
mitochondria getting transported around each cell, and connect to over 10,000
other neurons. That's a big social network!

Neurons are incredibly complicated organisms. Most of our DNA codes for neural
genes -- and neurons (and skin) are the default cell type (if an embryonic
stem cell isn't turned into something else).

[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5687842/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5687842/)

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jcims
If you have a little time to burn, Nick Moore has a few 'pond life' videos
that include the stentor. Here's one example:

[https://www.youtube.com/watch?v=PGSc_dIBfT8&t=896](https://www.youtube.com/watch?v=PGSc_dIBfT8&t=896)

If you fast forward a bit there's another shoot of one even more zoomed in,
but this one gives you an idea of the complexity of behavior in this little
critter.

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MrQuincle
Can someone explain the "hierarchical decision process"?

If it after K repeats of a stimuli it starts to do Y rather than X, this only
means that there is a state variable that gets over a threshold. A capacitor
so to say.

Or would we be able to say the same of a circuit with a cap?

