Connectionists: Brain-like computing fanfare and big data fanfare

[Chris McNorgan]

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.

http://www.ncbi.nlm.nih.gov/pubmed/24117388


  A Connectionist Approach to Mapping the Human Connectome Permits
  Simulations of Neural Activity Within an Artificial Brain.

McNorgan C 
<http://www.ncbi.nlm.nih.gov/pubmed?term=McNorgan%20C%5BAuthor%5D&cauthor=true&cauthor_uid=24117388>,Joanisse 
MF 
<http://www.ncbi.nlm.nih.gov/pubmed?term=Joanisse%20MF%5BAuthor%5D&cauthor=true&cauthor_uid=24117388>.


      Author information<http://www.ncbi.nlm.nih.gov/pubmed/24117388#>


      Abstract

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.



Cheers,
Chris McNorgan
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