pillow lab @ princeton

neural coding & computation group


adaptive stimulus selection for neural tuning curves - matlab code

description: Adaptive stimulus selection and Bayesian inference for neural tuning curves under two models of the response: 1) parametric tuning curve with Poisson noise; 2) nonparametric tuning curve parametrized by a transformed gaussian process (GP) and Poisson noise. The optimal stimulus given the stimuli and responses collected so far in the experiment is computed using either infomax (maximizing the expected gain in mutual information about the parameters) or uncertainty sampling (the stimulus location for which the posterior over the tuning curve has maximal variance).

download: master.zip   (more info: README.html)
browse: github project page

relevant publications:

  • Pillow JW & Park Mijung (2016). Adaptive Bayesian methods for closed-loop neurophysiology. In Closed Loop Neuroscience, ed. A. El Hady, Elsevier: 3-18. [abs | pdf | link | bibtex]

  • Park Mijung, Weller JP, Horwitz GD, & Pillow JW (2014). Bayesian active learning of neural firing rate maps with transformed Gaussian process priors. Neural Computation 26(8):1519-1541. [abs | pdf | SI | link ]

  • Please report any bugs to pillow@princeton.edu