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* main.md contains latest from brmullen, scweiser gdoc vers <https://docs.google.com/document/d/1VU3gaRKq3z3dN8ZNvDmwCUXAtIp4xkUgb4xXx5mhGvM> - that version was in turn a merged version from the biorxiv manuscripts contained in the methods-paper.git and ML-paper.git repos from 2020-2021 * jba added comments.md
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- Q: Should the title be the same as the methods-paper version published on biorxiv?
- Q: Is there conclusive message of wide interest that could be pointed to?
- Refresh, simplify abstract
- Q: Should data density/sampling over relevant scales be emphasized?
- Q: Should the statistics of our time series be commented on?
- Q: Would the isofl data help?
Abstract
- reread old abstracts and compare lines
- contrast 'supervised' with 'data-driven' | unsupervised
- def 'limited references' better
- the statistical model baseline
- statistical power of multivariate sufficiently dense sampling within a space-time window (scale | frame of reference | local viewport | field of view)
- replicative (repl.) information about 'segments independent functional units' and 'produce segmentations of the cortical surface' possibly (unless surface segmentations are cortical areas containing the units?)
- replicative (repl.) sentences, information at end of abstr
- Expand, rewrite (rw) to focus 'unique to each individual's functional patterning' better
- perhaps from first sentence
- the dense sampling in space and time from single individuals is key. The gaussian baseline estimate from long enough recordings. Compared to the group averaged studies and less-than-optimal baseline assumptions that are typically utilized in most studies and applications using either unsupervised or supervised ML implementations
- Expand on how this is optimal information extraction
- Single plane multivariate sensor
- widefield, pixelsize, reproduction ratio
Introduction
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because of lack of spatial density sampling
- def and expand this more
- can add same is true for many or most other applications of ICA or other eigendecomp routines in neuro- (and proabably most fields?)
- e.g. [^Mukamel:2009] ICA used with 2P laser scanning calcium imaging time series at microscale (cellular) level. Much lower data ingests
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IC model requires Gaussian baseline and independent message source
- must have one gaussian component
- many other investigations do inter-subject grouping
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message source independence
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def mesoscale observation
- Can it be more rigorously defined? If not, should a rough def be tied to something on sensor parameters
- CMOS array, pixel sampling, size, supraneuronal
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def What is baseline
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Specify what is underdeveloped
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expand on what has been done, utility of work till now, setting upfor the caveats later
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rm last line of first para.
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rw start of second para.
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rw start of third para.
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rw start of fourth para.
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merge ICA parts of third and fifth para.
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rw merge calcium imaging parts of third para. with that of first and second para.