7 ms·

A Connectome-Based Convolutional Network Model of the Drosophila Visual System

88 points by beefman 8y ago

deleted 8y ago
[deleted]
taneq 8y ago
> 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.
olliej 8y ago
Does anyone have a link to the supplemental material for this?
- rflrob 8y ago
  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 8y ago
    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)
nighthawk1 8y ago
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.
MrQuincle 8y ago
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.
andbberger 8y ago
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
- paradroid 8y ago
  It's theoretical neuroscience. If you have philosophical difference write a position paper.
  - andbberger 8y ago
    ?? I don't have a philosophical difference. I just don't believe they did their due diligence. Based on my personal experience in the lab where I was constantly pressured to do garbage science in the name of shiny results...
    - stochastic_monk 8y ago
      This sounds a little like a lab I was once part of. I’m now in an entirely different field of research.
    - paradroid 8y ago
      That's a philosophical difference. I don't disagree, but given the various constraints involved what you're asking isn't possible. Additionally, I'd note that it sounds like they are practicing Lean methodology - which is justifiable. https://plato.stanford.edu/entries/scientific-method/#MetPra https://plato.stanford.edu/entries/scientific-method/#MetPra
      - andbberger 8y ago
        I'm sorry, what am I asking?
mrfusion 8y ago
Is this implying these flies use gradient descent?
- andbberger 8y ago
  No.
mrfusion 8y ago
How do they measure the synaptic weights? I didn’t think even electron imagery could see synaptic receptors and neurotransmitters.
- andbberger 8y ago
  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
- DocSavage 8y ago
  You can see synaptic receptors (T-bars and post-synaptic densities) in EM, particularly if the staining is good. Neurotransmitter identification, not so much. So you combine the EM connectomics with other studies like RNA sequencing or Fluorescent In-Situ Hybridization. In insects, you can have multiple transmitters with multiple receptors (possibly differing in sign) in the same cell, so there's that complication.
jdc 8y ago
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-integration http://docs.openworm.org/en/0.9/Projects/muscle-neuron-integ...
yedawg 8y ago
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.
rdlecler1 8y ago
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.

88 points by beefman 8y ago

deleted 8y ago

[deleted]

> 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.

olliej 8y ago

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

rflrob 8y ago
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 8y ago
  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)

nighthawk1 8y ago

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.

MrQuincle 8y ago

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.

andbberger 8y ago

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

paradroid 8y ago
It's theoretical neuroscience. If you have philosophical difference write a position paper.
- andbberger 8y ago
  ?? I don't have a philosophical difference. I just don't believe they did their due diligence. Based on my personal experience in the lab where I was constantly pressured to do garbage science in the name of shiny results...
  - stochastic_monk 8y ago
    This sounds a little like a lab I was once part of. I’m now in an entirely different field of research.
  - paradroid 8y ago
    That's a philosophical difference. I don't disagree, but given the various constraints involved what you're asking isn't possible. Additionally, I'd note that it sounds like they are practicing Lean methodology - which is justifiable. https://plato.stanford.edu/entries/scientific-method/#MetPra https://plato.stanford.edu/entries/scientific-method/#MetPra
    - andbberger 8y ago
      I'm sorry, what am I asking?

mrfusion 8y ago

Is this implying these flies use gradient descent?

andbberger 8y ago
No.

mrfusion 8y ago

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

andbberger 8y ago
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
DocSavage 8y ago
You can see synaptic receptors (T-bars and post-synaptic densities) in EM, particularly if the staining is good. Neurotransmitter identification, not so much. So you combine the EM connectomics with other studies like RNA sequencing or Fluorescent In-Situ Hybridization. In insects, you can have multiple transmitters with multiple receptors (possibly differing in sign) in the same cell, so there's that complication.

jdc 8y ago

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-integration http://docs.openworm.org/en/0.9/Projects/muscle-neuron-integ...

yedawg 8y ago

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.

rdlecler1 8y ago

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.