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I receive these emails in digest form, so I wasn't on top of the
current conversation. Given that neural networks operate over
neuronally-inspired processing units, I always found the lack of
crosstalk between neuroscience and connectionist modeling
literatures surprising. Though not nearly as ambitious as the BRAIN
initiative, I've recently published a paper in Brain Connectivity
demonstrating the sorts of interesting things that can come out of
applying connectionist modeling techniques directly to Big Data from
resting state fMRI. I'm not sure what sort of reception it will
ultimately get from the neuroimaging community, but it seems topical
and perhaps it will be of some interest to readers of this mailing
list.<br>
<br>
<a href="http://www.ncbi.nlm.nih.gov/pubmed/24117388">http://www.ncbi.nlm.nih.gov/pubmed/24117388</a><br>
<br>
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-webkit-text-stroke-width: 0px; background-color: rgb(255, 255,
255);">A Connectionist Approach to Mapping the Human Connectome
Permits Simulations of Neural Activity Within an Artificial Brain.</h1>
<div class="auths" style="font-size: 0.923em; color: rgb(0, 0, 0);
font-family: arial, helvetica, clean, sans-serif; font-style:
normal; font-variant: normal; font-weight: normal; letter-spacing:
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background-color: rgb(255, 255, 255);"><a
href="http://www.ncbi.nlm.nih.gov/pubmed?term=McNorgan%20C%5BAuthor%5D&cauthor=true&cauthor_uid=24117388"
style="color: rgb(102, 0, 102); border-bottom-width: 0px;
text-decoration: underline;">McNorgan C</a>,<span
class="Apple-converted-space"> </span><a
href="http://www.ncbi.nlm.nih.gov/pubmed?term=Joanisse%20MF%5BAuthor%5D&cauthor=true&cauthor_uid=24117388"
style="color: rgb(102, 0, 102); border-bottom-width: 0px;
text-decoration: underline;">Joanisse MF</a>.</div>
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<h3 style="font-size: 1.0769em; line-height: 1.2857; margin: 0px;
color: rgb(152, 87, 53); font-weight: bold; display: inline;">Abstract</h3>
<div class="">
<p style="margin: 0px 0px 0.5em;">Abstract Data-driven models
drawn from statistical correlations between brain activity and
behavior are used to inform theory-driven models, such as
those described by computational models, which provide a
mechanistic account of these correlations. This article
introduces a novel multivariate approach for bootstrapping
neurologically-plausible computational models that accurately
encodes cortical effective connectivity from resting state
functional neuroimaging data (rs-fMRI). We show that a network
modularity algorithm finds comparable resting state networks
within connectivity matrices produced by our approach and by
the benchmark method. Unlike existing methods, however, ours
permits simulation of brain activation that is a direct
reflection of this cortical connectivity. Cross-validation of
our model suggests that neural activity in some regions may be
more consistent between individuals, providing novel insight
into brain function. We suggest this method to make an
important contribution toward modeling macro-scale human brain
activity, and it has the potential to advance our
understanding of complex neurological disorders and the
development of neural connectivity.</p>
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<br>
<br>
Cheers,<br>
Chris McNorgan
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