BI 227 Decoding Memories from Connectomes

Five experts meet to discuss progress, possibilities, and hurdles toward using modern and future technology to decode "nontrivial" memories from static connectomes.

Show notes: https://braininspired.co/podcast/227/

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Can you look at all the synaptic connections of a brain, and tell me one nontrivial memory from the organism that has that brain? If so, you shall win the $100,000 prize from the Aspirational Neuroscience group.
I was recently invited for the second time to chair a panel of experts to discuss that question and all the issues around that question - how to decode a non-trivial memory from a static map of synaptic connectivity.
Before I play that recording, let me set the stage a bit more.
Aspirational Neuroscience is a community of neuroscientists run by Kenneth Hayworth, with the goal, from their website, to "balance aspirational thinking with respect to the long-term implications of a successful neuroscience with practical realism about our current state of ignorance and knowledge." One of those aspirations is to decoding things - memories, learned behaviors, and so on - from static connectomes. They hold satellite events at the SfN conference, and invite experts in connectomics from academia and from industry to share their thoughts and progress that might advance that goal.
In this panel discussion, we touch on multiple relevant topics. One question is what is the right experimental design or designs that would answer whether we are decoding memory - what is a benchmark in various model organisms, and for various theoretical frameworks? We discuss some of the obstacles in the way, both technologically and conceptually. Like the fact that proofreading connectome connections - manually verifying and editing them - is a giant bottleneck, or like the very definition of memory, what counts as a memory, let alone a "nontrivial" memory, and so on.