Statistics Colloquium
Friday, April 11, 2014 · 11 AM - 12 PM
Speaker
Satish Iyengar
Statistics Department
University of Pittsburgh
Title
Regularized regression models for detecting neuronal interactions
Abstract
Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions are now collected readily in the laboratory. Generalized linear models are a platform for analyzing multi-electrode recordings of neuronal spike train data. We suggest an L1-regularized logistic regression model (L1L method) to detect short-term (order of 3 ms) neuronal interactions. We describe the computational aspects of this model, the results of simulation studies that indicate improvements over traditional cross-correlation methods, and application of our method to monkey dorsal premotor cortex.
Abstract
Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions are now collected readily in the laboratory. Generalized linear models are a platform for analyzing multi-electrode recordings of neuronal spike train data. We suggest an L1-regularized logistic regression model (L1L method) to detect short-term (order of 3 ms) neuronal interactions. We describe the computational aspects of this model, the results of simulation studies that indicate improvements over traditional cross-correlation methods, and application of our method to monkey dorsal premotor cortex.