Kameron Decker Harris

This paper develops a scalable approach to recover recurrent dynamics in neural time series data using low-rank tensors and demonstrates its application to simulated human brain connectivity.

This study uses a computational model of the preBotC network to show that variability in network topology and neuronal properties influences opioid-induced respiratory depression and recovery.

This study determines the optimal granule cell representation for cerebellar learning tasks beyond random stimulus discrimination, finding that dense representations are more optimal for continuous input-output transformations.

The paper models neuronal receptive fields as random samples from distributions, showing improved performance in sensory encoding tasks.

The paper constructs a high-resolution voxel-scale model of the mouse connectome using anterograde tracing data, outperforming previous regional models.