Description:

Please join us for the Applied Math Seminar on Monday, 2/18 at 4:00 PM in Tech M416.

Title: 

Generalized Linear Models of Spiking Neurons  

Speaker: Sara Solla, Northwestern University

Abstract: 

Generalized Linear Models provide a framework for the systematic description of neural activity. The formulation of these models is based on the exponential family of probability distributions; the Bernoulli and Poisson distributions are relevant to the case of stochastic spiking. In this approach, the time-dependent firing rate of individual neurons is modeled in terms of  experimentally accessible correlates of neural activity: patterns of activity of other neurons in the network, inputs provided through various sensory modalities or by other brain areas, and outputs such as muscle activity or motor responses. Model parameters are fit to maximize the likelihood of the  observed firing statistics; smoothness and sparseness constraints can be incorporated via regularization techniques. When applied to neural data, this modeling approach provides a powerful tool for mapping the spatiotemporal receptive fields of individual neurons, characterizing network connectivity through pairwise interactions, and monitoring synaptic plasticity.