Here’s a recap from the Scaling models for high-dimensional neural data at this year’s Cosyne. Unfortunately I couldn’t make it – I’m defending in a few days – but there were some really interesting talks.
I’m especially intrigued by Jeremy Freeman’s analysis of zebrafish whole-brain recordings. He has a tentalizing talk at a tech conference on this subject:
Scalable models for high-dimensional neural data
[ This blog post is collaboratively written by Evan and Memming ]
The Scalable Models workshop was a remarkable success! It attracted a huge crowd from the wee morning hours till the 7:30 pm close of the day. We attracted so much attention that we had to relocate from our original (tiny) allotted room (Superior A) to a (huge) lobby area (Golden Cliff). The talks offered both philosophical and methodological perspectives, reflecting diverse viewpoints on and approaches to high-dimensional neural data. Many of the discussions continued the next day in our sister workshop. Here we summarize each talk:
Konrad Körding – Big datasets of spike data: why it is coming and why it is useful
Konrad started off the workshop by posting some philosophical questions about how big data might change the way we do science. He argued that neuroscience is rife with theories (for instance, how uncertainty is…
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