Deconstructing Odorant Identity via Primacy in Dual Networks

Kepple, D. R., Giaffar, H., Rinberg, D., Koulakov, A. A. (February 2019) Deconstructing Odorant Identity via Primacy in Dual Networks. Neural Computation, 31 (4). pp. 710-737. ISSN 08997667 (ISSN)

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DOI: 10.1162/neco_a_01175


In the olfactory system, odor percepts retain their identity despite substantial variations in concentration, timing, and background. We study a novel strategy for encoding intensity-invariant stimulus identity that is based on representing relative rather than absolute values of stimulus features. For example, in what is known as the primacy coding model, odorant identities are represented by the conditions that some odorant receptors are activated more strongly than others. Because, in this scheme, the odorant identity depends only on the relative amplitudes of olfactory receptor responses, identity is invariant to changes in both intensity and monotonic nonlinear transformations of its neuronal responses. Here we show that sparse vectors representing odorant mixtures can be recovered in a compressed sensing framework via elastic net loss minimization. In a primacy model, this minimization is to be performed under the constraint that some receptors respond to a given odorant that is stronger than others. Using duality transformation, we show that such a constrained optimization problem can be solved by a neural network whose Lyapunov function represents the dual Lagrangian and whose neural responses represent the Lagrange coefficients of primacy and other constraints. The structure of connectivity in such a dual network resembles known features of connectivity in the olfactory circuits. We thus propose that networks in the piriform cortex implement dual computations to compute odorant identity with the sparse activities of individual neurons representing Lagrange coefficients. More generally, we propose that sparse neuronal firing rates may represent Lagrange multipliers, which we call the dual brain hypothesis. We show such a formulation is well suited to solve problems with multiple interacting relative constraints.

Item Type: Paper
Subjects: bioinformatics
bioinformatics > computational biology > algorithms
organism description > animal behavior
bioinformatics > computational biology
organism description > animal behavior > odor recognition
organism description > animal behavior > olfactory
CSHL Authors:
Communities: CSHL labs > Koulakov lab
School of Biological Sciences > Publications
Depositing User: Matthew Dunn
Date: 14 February 2019
Date Deposited: 20 Feb 2019 21:04
Last Modified: 29 Feb 2024 19:52
PMCID: PMC7449618
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