Deconstructing Odorant Identity via Primacy in Dual Networks

Kepple, Daniel, Giaffar, Hamza, Rinberg, Dmitry, Koulakov, Alexei (September 2016) Deconstructing Odorant Identity via Primacy in Dual Networks. arXiv. (Submitted)

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DOI: 10.48550/arXiv.1609.02202


In the olfactory system, odor percepts retain their identity despite substantial variations in concentration, timing, and background. We propose a novel strategy for encoding intensity-invariant stimuli identity that is based on representing relative rather than absolute values of the stimulus features. Because, in this scheme, stimulus identity depends on relative amplitudes of stimulus features, identity becomes invariant with respect to variations in intensity and monotonous non-linearities of neuronal responses. In the olfactory system, stimulus identity can be represented by the identities of the p strongest responding odorant receptor types out of a species dependent complement. We show that this information is sufficient to recover sparse stimuli (odorants) via elastic net loss minimization. Such a minimization has to be performed under constraints imposed by the relationships between stimulus features. We map this problem onto the dual problem of minimizing a functional of Lagrange multipliers. The dual problem, in turn, can be solved by a neural network whose Lyapunov function represents the dual Lagrangian. We thus propose that networks in the piriform cortex compute odorant identity and implement dual computations with the sparse activities of individual neurons representing the Lagrange multipliers.

Item Type: Paper
Subjects: organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
organism description > animal behavior > odor recognition
organism description > animal behavior > olfactory
CSHL Authors:
Communities: CSHL labs > Koulakov lab
School of Biological Sciences > Publications
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 7 September 2016
Date Deposited: 13 Oct 2023 14:03
Last Modified: 29 Feb 2024 19:53
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