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) 知觉对象的性质和导致其在大脑中表现的神经机制的性质是 神经科学中的基本问题之一。感知对象的表示是否填充在空间中 它们是低维的还是反映了刺激空间的复杂性？刺激的特点是什么？ 由感知空间的维度所代表？大脑中呈现的物体是如何保持的 关于刺激特征、时间和背景变化的不变性？ 尽管我们对嗅觉的分子基础的理解取得了实质性的进展，但对于 嗅觉系统，这些问题仍然没有答案。例如，在眼睛里，三个人的反应 视锥感光器的类型与人类色觉感知的三维空间相对应。 理解颜色空间的低维性质是我们理解颜色的基础 幻象。在嗅觉系统中，类似的概念理解是缺失的。 这个项目是理解嗅觉编码本质的协同工作的一部分。基于 由同一U19计划的其他项目收集的实验数据集以及公开提供的数据 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，以及 感知品质，建立一个在空间之间建立联系的神经网络模型，并解决 概念性的问题，使这个网络在生物学上具有真实性。使用最先进的机器学习 方法，我们将生成一个嗅觉系统的预测性计算模型作为交付。 我们的目标是开发、在计算模型中实现并测试至少两个关于 嗅觉编码的本质。首先，我们将检验这样一个假设，即嗅觉空间包含的空间要少得多 大小比气味感受器的类型(OR)的数量更多。我们的初步数据显示 与OR类型的约103个相比，主维度的数量可能低至10个。我们将对这些进行定义 并将它们与气味的分子特性联系起来。第二，我们将测试 首位编码假说，根据该假说，一小群最敏感的嗅觉的身份 受体类型以浓度不变的方式代表气味特性。这样的表示法呈现 气味物体对噪音有很强的抵抗力。我们的计算/理论研究将与 各试验组。我们的项目包括三个具体目标：目标1：构建预测计算 嗅觉刺激、反应和知觉空间的模型；目标2：开发预测网络模型 用于在嗅觉空间之间进行映射；以及目标3：建立生物上逼真的嗅觉网络模型。 由于感官对象的表征是神经科学中的一个基本问题，数学原理 我们的研究发现将阐明在其他感觉形式中感觉表征的原理。