Abstract

The brain faces many complex problems when dealing with odorant signals. Odours are multidimensional objects, which we usually experience as unitary percepts. They are also noisy and variable, but we can classify and identify them well. This means that the olfactory system must solve complicated pattern-learning and pattern-recognition problems. I propose that part of the solution relies on a particular architecture that imposes a dynamic format on odour codes. According to this hypothesis, the olfactory system actively creates a large coding space in which to place odour representations and simultaneously optimizes their distribution within it. This process uses both oscillatory and non-periodic dynamic processes with complementary functions: slow non-periodic processes underlie decorrelation, whereas fast oscillations allow sparsening and feature binding.

Author affiliations

1. California Institute of Technology, Division of Biology 139-74, 1201 East California Boulevard, Pasadena, California 91125, USA.
   Email: laurentg@caltech.edu