ArticleProceedings of the National Academy of Sciences of the United States of America2007
Processing and classification of chemical data inspired by insect olfaction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Decoding Smell from Receptor Structure.Research square · 2026Article
- Nonlinear high-activity neuronal excitation enhances odor discrimination.Current biology : CB · 2025Article
- Gain modulation and odor concentration invariance in early olfactory networks.PLoS computational biology · 2023Article
- Higher-order olfactory neurons in the lateral horn support odor valence and odor identity coding ineLife · 2022Article
- Neuromorphic Engineering Needs Closed-Loop Benchmarks.Frontiers in neuroscience · 2022Review
- Data-science based analysis of perceptual spaces of odors in olfactory loss.Scientific reports · 2021Article
- Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.Frontiers in neuroscience · 2021Article
- An unsupervised neuromorphic clustering algorithm.Biological cybernetics · 2019Article
- An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems.Sensors (Basel, Switzerland) · 2017Review
- Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.PLoS computational biology · 2017Article
- Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem.PloS one · 2015Article
- Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms.Frontiers in neuroscience · 2015Article
- Computational models to understand decision making and pattern recognition in the insect brain.Current opinion in insect science · 2014Article
- A neuromorphic network for generic multivariate data classification.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.Frontiers in neural circuits · 2014Article
- Parallel processing in the honeybee olfactory pathway: structure, function, and evolution.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2013Review
- Mixture and odorant processing in the olfactory systems of insects: a comparative perspective.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2013Review
- Parallel processing via a dual olfactory pathway in the honeybee.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
- Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules.Molecular informatics · 2013Article
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2 authors.
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Abstract
The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular representations and classify molecules. The three-step approach mimics neurocomputational principles observed in olfactory systems. In the first step, the original stimulus space is sampled by "virtual receptors," which are chemotopically arranged by a self-organizing map. In the second step, the signals from the virtual receptors are decorrelated via correlation-based lateral inhibition. Finally, in the third step, olfactory scent perception is modeled by a machine learning classifier. We found that signal decorrelation during the second stage significantly increases the accuracy of odorant classification. Moreover, our results suggest that the proposed signal transform is capable of dimensionality reduction and is more robust against overdetermined representations than principal component scores. Our olfaction-inspired method was successfully applied to predicting bioactivities of pharmaceutically active compounds with high accuracy. It represents a way to efficiently connect chemical structure with biological activity space.
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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.