Predicting computations necessary for the decoding of odor mixtures by the olfactory system

嗅觉系统解码气味混合物所需的预测计算

• 批准号: 10621161
• 负责人: Vijay Singh
• 金额: $ 14.4万
• 依托单位: NORTH CAROLINA AGRI & TECH ST UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10621161
• 关键词: Algorithms; Architecture; Behavioral; Biological; Biological Process; Biophysics; Chemicals; Complex; Complex Mixtures; Data; Environment; Individual; Measures; Modeling; Neural Network Simulation; Neurons; North Carolina; Odors; Olfactory Pathways; Organism; Performance; Physics; Process; Property; Research; Role; Sensory; Structure; System; Theoretical model; Universities; Work; biological information processing; biological systems; combinatorial; computerized data processing; experimental study; high dimensionality; individual response; information processing; neural; olfactory disorder; olfactory receptor; programs; receptor; response; student training

Our olfactory system processes complex odor mixtures, drawn from a very high dimensional space of over 10,000 possible odorants, using a limited set of (~100-1000) olfactory receptors. While a considerable amount of work has done to understand the odors are encoded by the olfactory receptors, the inverse problem: “How does the olfactory system obtain odor information from receptor response?” is unclear. This project aims to identify the computations that are necessary for the decoding odor information from receptor responses. We will develop decoding algorithms and mechanistic neural network models for decoding odor information from receptor responses. We will study the performance of these algorithms and mechanistic models and compare their predictions to the structure of the olfactory system and available data on the performance of organisms in olfactory behavioral tasks. Through such comparisons, we will identify the specific computations that are necessary for decoding of natural odors. This would be achieved through the following aims. Aim 1: Develop algorithms for decoding odor information from receptor responses and compare their performance to behavioral data. Aim 2: Develop biophysical neural network models the olfactory system and compare it to odor decoding algorithm to predict role of olfactory circuits.

我们的嗅觉系统处理复杂的气味混合物，这些气味混合物是从非常高的气味中提取的。 超过 10,000 种可能的气味剂的维度空间，使用有限的一组 (~100-1000) 虽然已经做了大量的工作来了解嗅觉受体。 气味是由嗅觉受体编码的，反问题是：“气味是如何产生的？” 嗅觉系统从受体反应中获取气味信息？”这个还不清楚。 该项目旨在确定解码气味所需的计算 我们将开发解码算法和来自受体反应的信息。 用于解码受体气味信息的机械神经网络模型 我们将研究这些算法和机械模型的性能。 并将他们的预测与嗅觉系统的结构和可用数据进行比较 通过这样的方式来研究生物体在嗅觉行为任务中的表现。 通过比较，我们将确定解码所需的具体计算 这将通过以下目标来实现：发展。 从受体反应中解码气味信息并比较它们的算法 目标 2：开发生物物理神经网络模型。 嗅觉系统并将其与气味解码算法进行比较以预测嗅觉回路的作用。

项目成果

What the odor is not: Estimation by elimination.

• DOI: 10.1103/physreve.104.024415
• 发表时间: 2021-08
• 期刊: Physical review. E
• 作者: Singh V;Tchernookov M;Balasubramanian V
• 通讯作者: Balasubramanian V