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Learning classification in the olfactory system of insects

journal contribution
posted on 2023-06-07, 20:55 authored by Ramón Huerta, Thomas NowotnyThomas Nowotny, Marta Garcia-Sanchez, H D I Abarbanel, M I Rabinovich
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 fo 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.

History

Publication status

  • Published

Journal

Neural Computation

ISSN

0899-7667

Issue

8

Volume

16

Page range

1601-1640

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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