54 lines
2.7 KiB
Markdown
54 lines
2.7 KiB
Markdown
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* Q: Should the title be the same as the methods-paper version published on biorxiv?
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- Q: Is there conclusive message of wide interest that could be pointed to?
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* Refresh, simplify abstract
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* Q: Should data density/sampling over relevant scales be emphasized?
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* Q: Should the statistics of our time series be commented on?
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* Q: Would the isofl data help?
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---
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## Abstract
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* [ ] reread old abstracts and compare lines
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* [ ] contrast 'supervised' with 'data-driven' | unsupervised
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* [ ] def 'limited references' better
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- the statistical model baseline
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- statistical power of multivariate *sufficiently dense* sampling within a space-time window (scale | frame of reference | local viewport | field of view)
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* [ ] 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?)
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* [ ] replicative (repl.) sentences, information at end of abstr
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* [ ] Expand, rewrite (rw) to focus 'unique to each individual's functional patterning' better
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- perhaps from first sentence
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- 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
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1. [ ] Expand on how this is optimal information extraction
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- Single plane multivariate sensor
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- widefield, pixelsize, reproduction ratio
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## Introduction
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2. because of lack of spatial density sampling
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- def and expand this more
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- can add same is true for many or most other applications of ICA or other eigendecomp routines in neuro- (and proabably most fields?)
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- 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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3. IC model requires Gaussian baseline and independent message source
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- must have one gaussian component
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- many other investigations do inter-subject grouping
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4. message source independence
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5. def mesoscale observation
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- Can it be more rigorously defined? If not, should a rough def be tied to something on sensor parameters
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- CMOS array, pixel sampling, size, supraneuronal
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6. 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.
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