Bayesian neural coding

We build models for how neural circuits can perform Bayesian inference. We test the models in the sound localization system of barn owls.

Relevant Publications

Seattle University undergraduate

Optimal prediction of moving sound source direction in the owl.
Cox, W., Fischer, B.J.
PLoS Comput Biol 11(7): e1004360. doi:10.1371/journal.pcbi.1004360, 2015.

Neural representation of probabilities for Bayesian inference.
Rich, D., Cazettes, F., Wang, Y., Pena, J.L., Fischer, B.J.
J Comp Neurosci. doi: 10.1007/s10827-014-0545-1, 2015.

Spatial cue reliability drives frequency tuning in the Barn Owl's midbrain.
Cazettes, F., Fischer, B.J.#, Pena, J.L.#
eLife doi: 10.7554/eLife.04854, 2014.
# Equal contribution

Population-wide bias of surround suppression in auditory spatial receptive fields of the owl's midbrain.
Wang, Y., Shanbhag, S.J., Fischer, B.J., Pena, J.L.
J Neurosci 32: 10470 - 10478, 2012.

Owl's behavior and neural representation predicted by Bayesian inference.
Fischer, B.J., Pena, J.L.
Nature Neurosci, 14: 1061-1066, 2011.

Bayesian estimates from heterogeneous population codes.
Fischer, B.J..
Proc. IEEE Int'l. Joint Conf. on Neural Networks. 2010.

Optimal models of sound localization by barn owls.
Fischer, B.J..
Advances in Neural Information Processing Systems 20, Cambridge, MA: MIT Press, 2008.

Relevant Senior Synthesis Projects

Simulating evolution of Tyto Alba auditory processing using Bayesian natural selection
Derrik Hanson, 2012.
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