Alex CAYCO GAJIC

My training is in applied mathematics, and for my dissertational research I used a combination of dynamical systems, information theory, and Ising models to understand how interactions between neurons affect how they encode and transmit information. For my postdoc I moved into an experimental cerebellar neuroscience lab where I learned data science tools for analysis of large-scale population recordings, especially dimensionality reduction.                      

My current research interests are centered around how interacting circuits in the brain control behavior. I focus on the cerebellum, but am interested in understanding how this region coordinates with the motor cortex and the basal ganglia during motor and cognitive learning. Towards this end I try to combine many tools including both reduced modelling and analytical methods, as well as data analysis together with my experimental collaborators. I am also very interested in new methods for dimensionality reduction which could help to identify how latent dynamics in neural population data emerge and evolve over learning.