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Mike and I discuss his modeling approach to study cognition. Many people I have on the podcast use deep neural networks to study brains, where the idea is to train or optimize the model to perform a task, then compare the model properties with brain properties. Mike's approach is different in at least two ways. One, he builds the architecture of his models using connectivity data from fMRI recordings. Two, he doesn't train his models; instead, he uses functional connectivity data from the fMRI recordings to assign weights between nodes of the network (in deep learning, the weights are learned through lots of training). Mike calls his networks empirically-estimated neural networks (ENNs), and/or network coding models. We walk through his approach, what we can learn from models like ENNs, discuss some of his earlier work on cognitive control and our ability to flexibly adapt to new task rules through instruction, and he fields questions from Kanaka Rajan, Kendrick Kay, and Patryk Laurent.

• The Cole Neurocognition lab.
• Twitter: @TheColeLab.
• Related papers
  • Discovering the Computational Relevance of Brain Network Organization.
  • Constructing neural network models from brain data reveals representational transformation underlying adaptive behavior.
• Kendrick Kay's previous episode: BI 026 Kendrick Kay: A Model By Any Other Name.
• Kanaka Rajan's previous episode: BI 054 Kanaka Rajan: How Do We Switch Behaviors?

0:00 - Intro