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Journal Articles Frontiers in Computational Neuroscience Year : 2022

Disorder and the Neural Representation of Complex Odors


Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from Drosophila . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.
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hal-03854608 , version 1 (15-11-2022)



Kamesh Krishnamurthy, Ann M Hermundstad, Thierry Mora, Aleksandra M Walczak, Vijay Balasubramanian. Disorder and the Neural Representation of Complex Odors. Frontiers in Computational Neuroscience, 2022, 16, ⟨10.3389/fncom.2022.917786⟩. ⟨hal-03854608⟩
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