Speaker
Sara A. Solla
(Northwestern University)
Description
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 case of 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 the maximum of a likelihood function
that is everywhere convex. In this talk, I will present the
theory of generalized linear models, derive equations for
likelihood maximization, and briefly discuss applications of
this approach to a variety of problems: the incorporation of
refractory effects in Poisson models, the mapping of
spatiotemporal receptive fields of individual neurons, the
characterization of network connectivity through directed
time-dependent pairwise interactions, and the monitoring of
plasticity.
