ArticleFrontiers in computational neuroscience2022
Disorder and the Neural Representation of Complex Odors.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 27 citations in OpenAlex.
- Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How the olfactory bulb maintains stable odor manifolds amid adaptation and representational drift.bioRxiv : the preprint server for biology · 2026Article
- Stable olfactory receptor activation across odor complexity.iScience · 2025Article
- Simultaneous detection and estimation in olfactory sensing.bioRxiv : the preprint server for biology · 2025Article
- When noncanonical olfaction is optimal.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.bioRxiv : the preprint server for biology · 2025Article
- Manifold learning for olfactory habituation to strongly fluctuating backgrounds.bioRxiv : the preprint server for biology · 2025Article
- Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes.bioRxiv : the preprint server for biology · 2025Article
- The primacy model and the structure of olfactory space.PLoS computational biology · 2024Article
- Slow and steady: auditory features for discriminating animal vocalizations.bioRxiv : the preprint server for biology · 2024Article
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.bioRxiv : the preprint server for biology · 2023Article
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.Advances in neural information processing systems · 2023Article
- Article
- Connectivity and dynamics in the olfactory bulb.PLoS computational biology · 2022Article
- Efficient processing of natural scenes in visual cortex.Frontiers in cellular neuroscience · 2022Review
- Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Environment-to-phenotype mapping and adaptation strategies in varying environments.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Front-end Weber-Fechner gain control enhances the fidelity of combinatorial odor coding.eLife · 2019Article
- Deconstructing Odorant Identity via Primacy in Dual Networks.Neural computation · 2019Article
- Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.