Learning classification in the olfactory system of insects.
We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size of the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.
Huerta
Ramón
R
Institute for Nonlinear Science, University of California San Diego, La Jolla CA 92093-0402, U.S.A. rhuerta@ucsd.edu
Nowotny
Thomas
T
García-Sanchez
Marta
M
Abarbanel
H D I
HD
Rabinovich
M I
MI
D000818:Animals; D003213:Conditioning, Psychological / Q000502:physiology*; D007313:Insecta / Q000502:physiology*; D016014:Linear Models; D008959:Models, Neurological; D024521:Mushroom Bodies / Q000166:cytology / Q000502:physiology*; D009474:Neurons / Q000145:classification / Q000502:physiology; D017711:Nonlinear Dynamics; D009833:Olfactory Pathways / Q000166:cytology / Q000502:physiology*