Department of Humanities and Social Sciences

Indian Institute of Technology Ropar


HSS Seminar

Regularized regression models for detecting neuronal interactions


Satish Iyengar

Department and Center for the Neural Basis of Cognition, University of Pittsburgh, USA 

Friday, May 24, 2013 at 11 AM
Venue : Lecture Hall 3
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 analyz ingmulti-electrode recordings of neuronal spike train data. We suggest an L1-regulariz edlogistic 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.

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