Category: GLMs

• Non-negative sparse priors

  Sparseness priors, which impose that most of the weights are small or zero, are very effective in constraining regression problems. The prototypical sparseness prior is the Laplacian prior (aka L1-prior), which imposes a penalty on the absolute value of individual weights. Regression problems (and GLMs) with Laplacian priors can be easily solved by Maximum a…

  January 5, 2012

• Integrate-and-fire neurons within the GLM framework

  Generalized linear models are very useful in modeling neural responses to dynamic stimuli. The Poisson-exponential GLM is the basis of many recent descriptions of responses in the retina, LGN and visual cortex. The Poisson-exponential GLM accounts for some aspects of neuronal data not well accounted for by earlier methods like reverse correlation; in particular, the…

  November 28, 2011