Lab’s latest at Nature Communications: "Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior", demonstrates empirically-estimated neural network models for insight into cognitive computations in human brain. 1/n This reports on an ambitious 5-year project to begin addressing fundamental cog. neuroscience questions with a generative/mechanistic approach. The key was to take our activity flow approach (colelab.github.io/ActflowToolbox/) to the next level by modeling both cognition & behavior. 2/n

Lab's latest out in @TrendsCognSci: "Discovering the Computational Relevance of Brain Network Organization". Unifies bio. & artificial neural nets via activity flow. Network coding models simulate how cognition emerges via connectivity. doi.org/10.1016/j.tics… (1/7) We realized even if all neurons' functions were mapped we still wouldn't know how the brain worked. Same goes for mapping all connections (connectome). We propose combining functional & connectivity maps, using predictive network models as "glue": network coding models. (2/7)

Just out at Nature Neuroscience, our multi-lab collaboration, "Advancing functional connectivity research from association to causation”. Unifies functional/effective conn. under goal of inferring causal interactions rdcu.be/bUfIw (a thread) Cognition emerges from network interactions, such that causal interactions should be central to studying brain function. Yet the research closest to this – functional connectivity – focuses primarily on association (correlation/coherence) rather than causation.

Lab’s latest pub out in NeuroImage, "Task activations produce spurious but systematic inflation of task functional connectivity estimates" doi.org/10.1016/j.neur…

Evoked activations inflate task functional connectivity, but corrected by subtracting mean evoked response An alternate link to the paper is on our lab's website: colelab.org/#publications