Predictive Computational Models of Olfactory Networks

嗅觉网络的预测计算模型

• 批准号: 10413210
• 负责人: ALEXEI KOULAKOV
• 金额: $ 29.33万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413210
• 关键词: 3-Dimensional; Address; Afferent Neurons; Algorithms; Architecture; Biological; Brain; Cellular Structures; Code; Collaborations; Color; Color Perception; Color Visions; Computer Models; Data; Data Set; Databases; Dimensions; Eye; Goals; Human; Knowledge; Learning; Light; Machine Learning; Mathematics; Methods; Modality; Modeling; Molecular; Nature; Neural Network Simulation; Neurons; Neurosciences; Noise; Odorant Receptors; Odors; Olfactory Pathways; Perception; Process; Property; Psychological reinforcement; Publications; Reporting; Response to stimulus physiology; Retinal Cone; Route; Sensory; Smell Perception; Source; Space Models; Stimulus; Structure; Synapses; System; Testing; Theoretical Studies; Training; Variant; Visual system structure; Weight; Work; artificial neural network; base; cell type; cohort; computational network modeling; data space; experimental group; experimental study; high dimensionality; insight; network architecture; network models; novel; olfactory receptor; olfactory stimulus; performance tests; piriform cortex; predictive modeling; programs; receptor; relating to nervous system; response; theories

SUMMARY (Project 5) The nature of perceptual objects and of the neuronal mechanisms leading to their representation in the brain is one of the fundamental questions in neuroscience. Do representations of perceptual objects populate spaces of low dimensionality or do they mirror the complexity of the stimulus space? What features of the stimulus are represented by the dimensions of the perceptual space? How can objects represented in the brain retain invariance with respect to variations in stimulus features, timing, and background? Despite substantial progress in our understanding of the molecular basis of the sense of smell, for the olfactory system, these questions remain unanswered. In the eye, for example, the responses of the three types of cone photoreceptors correspond to the three dimensions sensed by human color vision. Understanding the low dimensional nature of color space was fundamental to our understanding of color vision. In the olfactory system, a similar conceptual understanding is missing. This project is a part of synergistic effort to understand the nature of olfactory coding. Based on experimental datasets collected by other projects of the same U19 program as well as publically available datasets, we will study the structure of the spaces of olfactory stimuli, responses of olfactory neurons, and perceptual qualities, build a neural network model that establishes connections between spaces, and resolve conceptual questions to make this network biologically realistic. Using state-of-the-art machine learning approaches, we will generate a predictive computational model of the olfactory system as a deliverable. Our goal is to develop, implement in a computational model, and test at least two theoretical ideas about the nature of olfactory code. First, we will test the hypothesis that olfactory spaces contain substantially fewer dimensions than the number of types of odorant receptors (OR). Our preliminary data indicates that the number of principal dimensions may be as low at 10, compared to ~103 of OR types. We will define these dimensions mathematically and relate them to the molecular properties of odorants. Second, we will test the primacy coding hypothesis, according to which identities of a small cohort of the most sensitive olfactory receptor types represent odorant identity in a concentration-invariant manner. Such representations render odor objects robust to noise. Our computational/theoretical studies will be carried out in close collaboration with the experimental groups. Our project includes three Specific Aims: Aim 1: To build predictive computational models for spaces of olfactory stimuli, responses, and percepts; Aim 2: To develop a predictive network model for mapping between olfactory spaces; and Aim 3: To build biologically realistic models of olfactory networks. Since representation of sensory objects is a fundamental problem in neuroscience, mathematical principles uncovered by our studies will elucidate the principles of sensory representation in other sensory modalities.

摘要（项目5） 感知对象的性质和导致其在大脑表示的神经元机制的性质是 神经科学中的基本问题之一。执行感知对象的表示空间 低维度或它们是否反映了刺激空间的复杂性？刺激的特征是什么 以感知空间的尺寸为代表？大脑中表示的对象如何保留 关于刺激特征，时机和背景的变化的不变性？ 尽管我们对嗅觉的分子基础的理解取得了长足的进步，但 嗅觉系统，这些问题仍未得到解决。例如，在眼中，这三个反应 锥形感受器的类型对应于人类色觉感受的三个维度。 了解颜色空间的低维质对于我们对颜色的理解至关重要 想象。在嗅觉系统中，缺少类似的概念理解。 该项目是了解嗅觉编码本质的协同努力的一部分。基于 同一U19程序的其他项目收集的实验数据集以及公开可用的 数据集，我们将研究嗅觉刺激空间的结构，嗅觉神经元的反应和 感知质量，建立一个神经网络模型，该模型在空间之间建立联系并解决 使该网络在生物学上现实的概念问题。使用最先进的机器学习 方法是，我们将生成嗅觉系统的预测计算模型作为交付。 我们的目标是在计算模型中开发，实施，并至少测试有关 嗅觉代码的性质。首先，我们将测试嗅觉空间较少的假设 维度比气味受体的类型（OR）的数量。我们的初步数据表明 主尺寸的数量可能在10时低于10或类型的〜103。我们将定义这些 数学上的尺寸将其与气味剂的分子特性相关联。第二，我们将测试 首要编码假设，根据哪种最敏感嗅觉的小组的身份 受体类型以浓度不变的方式代表气味身份。这样的表示形式 气味对噪音的强大对象。我们的计算/理论研究将与 实验组。我们的项目包括三个特定目标：目标1：建立预测计算 嗅觉刺激，反应和感知空间的模型；目标2：开发预测网络模型 用于在嗅觉空间之间进行映射；目标3：建立嗅觉网络的生物学现实模型。 由于感觉对象的表示是神经科学的基本问题，因此数学原理 我们的研究发现将阐明其他感觉方式中的感觉表示原理。