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The classification problem in the olfactory system of insects
Ramon Huerta, Thomas Nowotny, Marta Garcia-Sanchez, Henry Abarbanel,
and Misha Rabinovich
UCSD
The olfactory system of the locust has a puzzling variation of coding
schemes throughout its various information relay stations. The
identity-coded information of the Antenna is compressed into
spatio-temporal patterns in the antennal lobe that are relayed towards
the mushroom body where they are transformed into a series of
quasi-static snapshots of sparse activity. The understanding of these
coding schemes is recently starting to emerge. The spatio-temporal
coding in the Antennal Lobe seems to be important for at least two
important functions: a) learning and memory and b) robust
discrimintation of similar odors. In this talk we concentrate on the
later stages of processing in particular on the advantages of sparse
coding and on an adequate network design for classification purposes.
To this end we will for now only consider single snapshots of Kenyon
cell activity. The classification task is then accomplished in two
steps. The first step is a nonlinear expansion from the Antennal Lobe
to the Kenyon cells in the Mushroom Body. This nonlinear projection
into a higher dimensional space allows information conservation via
sparse coding even if carried out with a non-specific connectivity
matrix. In the second phase the sparse code on the Kenyon cell screen
is linearly classified in the next processing layer. The neurons that
perform this linear classification are hyperplanes whose connections
are tuned by local Hebbian learning and competition due to mutual
inhibition. We show that the number of Kenyon cells and the degree of
sparseness in coding are crucial elements for efficient
classification.