Owl's behavior and neural representation predicted by Bayesian inference

Brian J Fischer (1) , Jose Luis Pena (1)

1 Departement d'Etudes Cognitives

The owl captures prey using sound localization. In the classical model, the owl infers sound direction from the position in a brain map of auditory space with the largest activity. However, this model fails to describe the actual behavior. While owls accurately localize sources near the center of gaze, they systematically underestimate peripheral source directions. Here we demonstrate that this behavior is predicted by statistical inference, formulated as a Bayesian model that emphasizes central directions. We propose that there is a bias in the neural coding of auditory space, which, at the expense of inducing errors in the periphery, achieves high behavioral accuracy at the ethologically relevant range. We then show that the owl's map of auditory space decoded by a population vector is consistent with the behavioral model. Thus, a probabilistic model describes both how the map of auditory space supports behavior and why this representation is optimal.

Mots clés: Sensory Systems; Behaviour; Computational Neuroscience

Brian J Fischer, Jose Luis Pena. Owl's behavior and neural representation predicted by Bayesian inference. Nature Neuroscience, 2011, ⟨10.1038/nn.2872⟩. ⟨hal-00655883⟩