slarson
commented
8 years ago Here are a couple of papers to add inspiration to this:
From the connectome to brain function - Bargmann, Marder - 2013.pdf Multiple models to capture the variability in biological neurons and networks - Marder, Taylor - 2011.pdf
What I would suggest we are seeking is a theory of how modifying underlying dynamical elements of a neuronal network affects its global behavior. The PCA'd picture gives us a low-dimensional cartoon of what is happening in the high-dimensional space. In order to build a theory of how the dynamical elements make up the global behavior, I think we may need to do a systematic survey of the effect of how modifying different dynamic elements change the behavior, as is suggested in the papers above (specifically the 2011 paper).
In addition to changing the static parameters of the model and seeing what happens; we could also take a model with a given set of parameters and see how perturbing different neurons with different current injections affects the global dynamics.
The goal would be to see the difference that make a difference -- which are the parameters that seem to have the largest effects when you change them? Which are the neurons that, when perturbed, change the global behavior the most? We could start with looking for the most obvious changes, and then working our way down to more subtle changes.
Armed with those observations, I think we may be able to begin to put together the start of a theory that could hopefully be generalized beyond this one nervous system!
One of the goals of
neuronal-analysisis to be the project-wide platform for analyzing both in-vivo and in-silico neuronal activity series data. The remarkable part of starting this repo now is the availability of both extensive time series data (from Kato) as well as a full and correctly mapped connectome (which is fairly recent). This opens up the possibility for groundbreaking work to be done in (1) parameterizing comprehensive models of C Elegans with realistic global and local dyamics, and (2) gaining new insight into the function of neural circuitry in CE. I hope this repo is where such work can begin.Because the space of what can be done with time series data is tremendous, I'm wondering if we can start a discussion of what people think will be useful analysis functions to implement here?
The original Kato paper used PCA on its datasets to correlate global neuronal dynamics with movement behavior. I'm positive there are a plethora of other techniques that can be employed both to analyze individual datasets, to compare datasets, and to relate dynamics to connectivity. We can look at the data statistically, with dynamical systems, etc.
What are your thoughts?