
A Connectome-Based Convolutional Network Model of the Drosophila Visual System - beefman
https://arxiv.org/abs/1806.04793
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andbberger
Funny to see this in light of our conversation yesterday [1]

This should be read with extreme skepticism. I have experienced first hand how
'science' is done in the Turaga lab. I have on multiple occasions been
pressured to cut corners and do shady things in the name of results.

In light of my experience, I require extra-extraordinary evidence to believe
anything coming out of that lab....

[1]
[https://news.ycombinator.com/item?id=17306673](https://news.ycombinator.com/item?id=17306673)

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joe_the_user
Well, also this claim is definitely something that would represent an
extraordinary advance. Any detailed correspondence between machine learning
neural nets and actual neurology would be quite a step and not something
neurologist or machine learning experts expect.

~~~
paradroid
Neurology is the study of disorders of the nervous system. This is theoretical
neuroscience. FTFY.

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jdc
If you're interested in this kind of thing, check out Openworm and their c302
neural network.

[http://docs.openworm.org/en/0.9/Projects/muscle-neuron-
integ...](http://docs.openworm.org/en/0.9/Projects/muscle-neuron-integration)

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rdlecler1
This is not dissimilar to research done on artificial regulatory networks
driving paterning in drsosophila. Turns out the topological circuitry, not the
biological details explains the behavior. The network actually didn’t work
initially until they added a then unknown gene that they later discovered.
They even used this artificial network to predict phenotypic mutations that
were later confirmed emirically.

One of the reasons why we have such a problem getting our heads around neural
networks is that we don’t test and remove the spurious interactions in our
topological visualization. Do that and the underlying circuit will reveal
itself in the same way that any second year EE can identify the circuit
topology of a 3-bit adder. Neural networks don’t have binary logic gates,
rather you can have a large number of inputs and it works on a threshold
basis.

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taneq
> Our work is the first demonstration, that knowledge of the connectome can
> enable in silico predictions of the functional properties of individual
> neurons in a circuit, leading to an understanding of circuit function from
> structure alone.

That's kind of huge. If they're right, the connectome from a real organism is
a good geometry for an ANN trained to do the same job.

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MrQuincle
Two kind of performance metrics they seem to suggest on top of accurate
tracking:

\+ stability of the network, the neurons should not change their function
willy nilly

\+ robustness against multiplicative noise

I'm curious if that might lead to a more physiological plausible network.

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nighthawk1
This is really the holy grail that we will be able to translate the connectome
into a software representation of it and be able to quickly acquire working
neural networks from living organisms.

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mrfusion
How do they measure the synaptic weights? I didn’t think even electron imagery
could see synaptic receptors and neurotransmitters.

~~~
andbberger
They don't. It's purely topology, for which they use EM [1]

Uncle Howard has spent a lot of money on modeling the fly connectome at this
point!! Pressure is on to show that connectome data is actually useful.

I am skeptical. It's not not useful. But it's just one piece of the puzzle...

[1] [https://www.janelia.org/project-
team/flyem](https://www.janelia.org/project-team/flyem)

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yedawg
Using recurrent neural nets to map and train ai based on part of the visual
connectome of a fly is interesting but lacks scalable experimental
feasibility. Every new neuron in the algorithm introduces an exponential
amount of complexity to the ai which takes away from it's scalability.

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mrfusion
Is this implying these flies use gradient descent?

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andbberger
No.

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olliej
Does anyone have a link to the supplemental material for this?

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rflrob
I can't find one, and weirdly the NIPS 2017 site doesn't seem to know about
this paper either...
[https://nips.cc/Conferences/2017](https://nips.cc/Conferences/2017)

~~~
jtmcmc
so I can say that they may submitted it to arxiv using a nips LaTeX styling
template - even if it wasn't in NIPS (could have been rejected or something
else and they forgot to change the style template)

