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  • Jun 03 2019 21:43
    Uiuran commented #15
  • May 28 2019 12:58
    Uiuran commented #15
  • Mar 05 2017 06:18

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  • Jun 15 2016 12:06
    theideasmith commented #17
  • Jun 14 2016 18:40
    lukeczapla commented #17
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Stephen Larson
@slarson
Probably a first thing would be to see if c302NetTuner.py actually works currently, and then start some issues to get it into a working state if it doesn't
We can also use that as inspiration and depart from it and write something much simpler to get intuitions
Akiva Lipshitz
@theideasmith
A better optimization would compare simulated data to real data, but the kato datasets lack many neuron labells. I was thinking we could use machine learning classification and combinatorial optimization to label unlabelled neurons in the kato-datasets if a certain unlabelled time series has a high probability of being a labelled neuron in another dataset.
Stephen Larson
@slarson
It may be possible to narrow down which neurons the unlabelled ones are if we ask the right questions about the prep
:)
Chee Wai Lee
@cheelee
Hi I have a quick question on those 5 datasets ... are there any worm behavior context associated with each of them over the 18 minutes of time? They look like they are different, but I have also noticed that the order of the neurons-specific data along the Y-axis are different across the datasets. It might help if they had the same presented order, so either human eyes can pick out more patterns, or some sort of image differential analysis could key in on areas of difference between each of those datasets.
Akiva Lipshitz
@theideasmith
Yea - as of now we don't have behavioral data. We could ask L
Saul Kato for behavioral data used in his paper
Additionally, the plots you see have clustered the neurons, so they cluster differently in the different datasets. I've also got code to generate uniformly ordered plots. For those plots, I was just wondering how the clusters compare across the datasets. And welcome to neuronal analysis! Excited people are interested.
Akiva Lipshitz
@theideasmith
shared_datasets.jpg
If you'd like, here are the datasets plotted with uniform ordering; you do lose clustering, but gain cross-dataset visual comparability
shared_datasets.jpg
Akiva Lipshitz
@theideasmith
Inspired by DevoWorm, I've made a project waffle.io board: https://waffle.io/openworm/neuronal-analysis
Akiva Lipshitz
@theideasmith
/all I think it would be productive to have a project meeting sometime. When are people available?
Stephen Larson
@slarson
Yeah let's do it! Maybe next Friday?
Akiva Lipshitz
@theideasmith
That would work for me.
Stephen Larson
@slarson
@theideasmith hey actually are you around now?
Akiva Lipshitz
@theideasmith
Yeah
Stephen Larson
@slarson
OK — hangout in like 10 minutes?
Akiva Lipshitz
@theideasmith
My previous message didn't go through. I'm not able to talk today. Monday?
Stephen Larson
@slarson
Ah sorry about that! No worries. Monday is tight for me; Friday is safest to plan for now but maybe we can do something ad hoc on Monday
Stephen Larson
@slarson
@/all sent an invite for Friday at 2pm ET for a chance to sync. If others want to join, just message me here in the channel
By the way, found this paper and it seems really relevant: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000190
Akiva Lipshitz
@theideasmith
@slarson can you resend the invite?
Stephen Larson
@slarson
Yes; doing now
Stephen Larson
@slarson
Thanks for making that waffle board @theideasmith. Was just updating it.
Akiva Lipshitz
@theideasmith
timeseries0.jpg
Initial simulations of model used in Organization of Excitable Dynamics in Hierarchical Biological Networks
Luke Czapla
@lukeczapla
It's pretty interesting results and I just made a minor change to align the model exactly with the paper. I haven't had luck with PyOpenWorm so the CElegansNeuronTable.xls was used here. I think tuning simple models with experimental data is a good plan to demonstrate the proof of principle but this one is too simplistic. If individual neurons at least had a few parameters (individual f and p values is a good start - it'd give a coarse-grained picture of neuronal excitability and recovery) then we could extract information by fitting to the kato data and do a full scientific simulation study
Stephen Larson
@slarson
@theideasmith @lukeczapla Wow :) OK we should find a time to get together and discuss these. This week I’m all booked up; how’s your next week looking?
Akiva Lipshitz
@theideasmith
Any day next week
Akiva Lipshitz
@theideasmith
The model I used to generate the figure above was written incorrectly. Here's what it really should look like:
timeseries.jpg
Akiva Lipshitz
@theideasmith
Three State Model on Multiple Graph Types, with same number of nodes
Multiple_Graphs_Sim.jpg
Stephen Larson
@slarson
I totally want to know more!
Ok @lukeczapla any constraints on day next week?
Akiva Lipshitz
@theideasmith
/all any time to meet tomorrow or Friday?
Stephen Larson
@slarson
Hey hey you should have an invite from me already
Maybe i sent it to the wrong address?
It is for friday 3pm ET
Akiva Lipshitz
@theideasmith
Ok great
Stephen Larson
@slarson
@theideasmith @lukeczapla ready when you are
Stephen Larson
@slarson
@theideasmith @lukeczapla Sorry we couldn’t connect today
Luke and I were chatting a little on here
He was saying:
@lukeczapla
yes, I think we could come up with a scan of all of f and p space
there are some neurons (<20) that they took out of the model, other than that it should be the same thing
Maybe let’s kick back and forth where we are on here
Luke Czapla
@lukeczapla
that's for comparison to the paper
Stephen Larson
@slarson
I’d still be up to sync for an hour over the weekend if there is a better time