I combine modelling and data analysis to understand the link between the activity of populations of neurons and behavior.
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.
Lanore F*, Cayco Gajic NA*, Gurnani H, Coyle D, Silver RA (2021). Cerebellar granule cell axons support high dimensional representations. Nature Neuroscience 24, 1142-1150.
Cayco Gajic NA, Silver RA (2019) Re-evaluating circuit mechanisms underlying pattern separation. Neuron 101, 584-602.
Cayco Gajic NA, Zylberberg J, Shea-Brown E (2018) A maximum entropy model for fitting higher-order interactions. Entropy 20, 489.
Cayco Gajic NA, Clopath C, Silver RA (2017) Sparse synaptic activity is required for decorrelation and pattern separation in feedforward networks. Nature Communications 8, 1116.
Cayco Gajic NA, Zylberberg J, Shea-Brown E (2015) Triplet correlations among similarly tuned cells impact population coding. Frontiers in Computational Neuroscience 9, 57.
Cayco Gajic NA, Shea-Brown E (2013). Neutral stability, rate propagation, and critical branching in feedforward networks. Neural Computation 25, 1768-1806