system level knowledge representation in neuroscience

Greets all

as promised today I started writing a few paragraphs for a report that I
hope to complete before the end of the month, to summarize the thinking
done in in the last few years as shared with this W3C AI KR CG

I have been writing a few system level paper such as

System Level Knowledge Representation,
https://www.screencast.com/t/MaaAHv5tbVL3
apologies if I repeat myself

More importantly, today a great paper supports the emergence of a  System
Level KR
showing the novel construct is in going the right direction

Enjoy this weekend read

Extracting representations of cognition across neuroimaging studies
improves brain decoding1

   - https://doi.org/10.1371/journal.pcbi.1008795


https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008795

The success of using distributed representations to bridge cognitive tasks
supports a system-level view on how brain activity supports cognition.

Our multi-study model will become increasingly useful to brain imaging as
the number of available studies grows. Such a growth is driven by the
steady increase of publicly shared brain-imaging data, facilitated by
online neuroimaging platforms and increased standardization [2
<https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008795#pcbi.1008795.ref002>
, 85
<https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008795#pcbi.1008795.ref085>].
With a larger corpus of studies, the proposed methodology has the potential
to build even better universal priors that overall improve statistical
power for functional brain imaging. As such, multi-study decoding provides
a path towards knowledge consolidation in functional neuroimaging and
cognitive neuroscience