- input / output / inner
- what
- keep only A->D neurons
- neurons or edges?
- identify "latent structure" (clustering, should be feedforward)
- how shoulw we constrain the clustering to be "feedforward"?
- then add others synapse types, how does structure change?
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look at PN -> KC "pattern"
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is that "pattern" present elsewhere?
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where in the brain do L and R NOT match
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central complex
- why do they care about this?
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circuit motif discovery
- how?
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are there any other groups of neurons "like" kenyon cells
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eg, that they can't be uniquely identified for some reason (eg, doesn't exist on other hemisphere)
- most other cells are individually matched across brain.
- we can directly send results also to AC/MZ
- I think I have been?
- ch 1: 3 layers, what structure is in there?
- what are the 3 layers?
- ch 2: 4 colors, what kind of circuit motifs are there together and different?
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how is larval dataset different from adult dataset??
- does this mean we will have the adult data?
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cross hemispheric differences
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inter-hemisphere
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synapse type differences
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multi-sensory/inner/motor (the adults don't have the complete proprioceptive)
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efference copy / proprioceptive
do we have the labels? modality? input? output? - we know sensory modality for PN and thats all they have given us
help AC a lot in short term:
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color the pathways, these are 21 ORNs, etc. color each group,
- what does color each group mean
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see the merged colors, which neurons are getting input from each of these types of inputs,
- I could certainly look at the proprotion of input onto a non PN from each PN type
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until eventually reaching the command neurons?
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how is MB injected into these paths of convergence?
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AC expects: 2 groups, regular sensory, the other is mostly proprioceptive which has its own circuits
- proprioceptive should be coming from the other direction?
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the "larvunculous".
- what's that
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closer to output layer proprioceptive convergences with other sensory
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there are 12 segments, somatosensory neurons is largest, each segment with 40 per hemisphere, so 40x2x12 = 100 neurons!
- are you talking about lower in the body?
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efference copies:
- the descending neurons which also project into the brain?
- neurons that synapse onto the descending neurons?
- eg, output and input of descending neurons
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stratified thing, AC expects layers (only clear layers are input and output layers), between these layers there will be feedback. much of this feedback is A->A, that is, subsequent layer impacts previous layer via A->A
- why do they think this?
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how many layers? maybe 6 or 7, but there are shortcuts.
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what is the diameter of the graph? expectation: 10 for A->D.
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feedback?
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are there hubs? is it a powerlaw?w
- powerlaw degree distribution?
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we can also compare to c. elegans!
- cool, scott's?
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Q1: what is info flow from input to output?
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Q2: you can have many parallel pathways per layer?