DeepNose: An Equivariant Convolutional Neural Network Predictive Of Human Olfactory Percepts

• URL: http://arxiv.org/abs/2412.08747v1
• Date: Wed, 11 Dec 2024 19:35:24 GMT
• Title: DeepNose: An Equivariant Convolutional Neural Network Predictive Of Human Olfactory Percepts
• Authors: Sergey Shuvaev, Khue Tran, Khristina Samoilova, Cyrille Mascart, Alexei Koulakov,
• Abstract summary: We train a convolutional neural network (CNN) to predict human percepts from semantic datasets. Our network offers high-fidelity perceptual predictions for different olfactory datasets. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts.
• Score: 0.9187505256430946
• License:
• Abstract: The olfactory system employs responses of an ensemble of odorant receptors (ORs) to sense molecules and to generate olfactory percepts. Here we hypothesized that ORs can be viewed as 3D spatial filters that extract molecular features relevant to the olfactory system, similarly to the spatio-temporal filters found in other sensory modalities. To build these filters, we trained a convolutional neural network (CNN) to predict human olfactory percepts obtained from several semantic datasets. Our neural network, the DeepNose, produced responses that are approximately invariant to the molecules' orientation, due to its equivariant architecture. Our network offers high-fidelity perceptual predictions for different olfactory datasets. In addition, our approach allows us to identify molecular features that contribute to specific perceptual descriptors. Because the DeepNose network is designed to be aligned with the biological system, our approach predicts distinct perceptual qualities for different stereoisomers. The architecture of the DeepNose relying on the processing of several molecules at the same time permits inferring the perceptual quality of odor mixtures. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts and help identify molecular features responsible for odor quality.

Related papers

• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities. We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features [0.0]
  We present an interpretable neural network trained on the UPWINS spectral library which contains spectra with rich metadata across variation in species, health, growth stage, annual variation, and environmental conditions for 13 selected indicator species and natural common background species. We show that the neurons in the network learn spectral indicators for chemical and physiological traits through visualization of the network weights, and we show how these traits are combined by the network for species identification with an accuracy around 90% on a test set.
  arXiv  Detail & Related papers  (2024-07-14T21:20:37Z)
• Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers [0.0]
  We present a versatile approach that uses the intermediate information of a universal neural network potential as a general-purpose descriptor for chemical property prediction. We show that transfer learning with graph neural network potentials such as M3GNet and MACE achieves accuracy comparable to state-of-the-art methods for predicting the NMR chemical shifts.
  arXiv  Detail & Related papers  (2024-02-28T15:57:22Z)
• Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels [56.69755544814834]
  Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers.
  arXiv  Detail & Related papers  (2024-01-25T19:05:53Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• Permutation Equivariant Neural Functionals [92.0667671999604]
  This work studies the design of neural networks that can process the weights or gradients of other neural networks. We focus on the permutation symmetries that arise in the weights of deep feedforward networks because hidden layer neurons have no inherent order. In our experiments, we find that permutation equivariant neural functionals are effective on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-02-27T18:52:38Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)
• Deep learning and high harmonic generation [0.0]
  We explore the utility of various deep neural networks (NNs) when applied to high harmonic generation (HHG) scenarios. First, we train the NNs to predict the time-dependent dipole and spectra of HHG emission from reduced-dimensionality models of di- and triatomic systems. We then demonstrate that transfer learning can be applied to our networks to expand the range of applicability of the networks.
  arXiv  Detail & Related papers  (2020-12-18T16:13:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.