CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional space

CRCNS：大脑用于在高维空间中标记点的常用算法策略

• 批准号: 10058965
• 负责人: Saket Navlakha
• 金额: $ 36.5万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-21 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058965
• 关键词: CRCNS; Common; algorithmic; strategies; used

The first major goal of this work is to learn how certain brain regions (olfactory system, hippocampus, and cerebellum) learn very complex stimuli that employ a combinatorial code to identify stimuli as points in a high-dimensional space. For example, the simple fruit fly olfactory system uses the firing rates of 50 different types of odorant receptors to identify each odor by placing it at a point in a SO-dimensional space. Although the fly olfactory system is well understood, less is known about analogous regions in vertebrate brains, and our goal is to begin to learn about these other regions. The first step is to start with the mouse olfactory system that is similar to the fly in some ways but has complexities that are absent in insects. These complexities include an enhanced ability to handle noise in the odor and to learn over time to discriminate between very similar odors (e.g., two types of red wines). Preliminary evidence shows that it should be possible to learn the role of these complexities in vertebrate olfaction. The research design involves studying the anatomy and recording the firing rates of different types of neurons at different levels of the mouse olfactory system and in applying computational methods and algorithms that have proved successful in earlier work to describe these complexities. The second major goal is to use insights into how these brain regions operate to improve the function of computer algorithms. For a long time, a dream of many neuroscientists and computer scientists has been to understand how the brain works well enough that we could translate insights from the brain to improve machine computation. Indeed, experience has shown that the brain has evolved novel variations of information processing algorithms used by computer scientists to solve general computational problems. With sufficient insight into algorithms used by the brain, these insights may provide unexpected ways to improve the function computer science algorithms. Further, understanding the circuit mechanisms involved in olfactory processing can help illuminate the basis of a variety of smell disorders, and may in the future lead to the construction of artificial smelling devices.

这项工作的第一个主要目标是了解某些大脑区域（嗅觉系统， 海马和小脑）学习采用组合代码的非常复杂的刺激 将刺激识别为高维空间中的点。例如，简单的果蝇 嗅觉系统使用50种不同类型的气味受体的发射速率 通过将其放置在某个维度空间中的点来识别每种气味。虽然飞嗅觉 系统知之甚少，对脊椎动物大脑中的类似区域知之甚少，我们的 目标是开始了解其他地区。第一步是从鼠标开始 嗅觉系统在某些方面与苍蝇相似，但具有复杂性 昆虫不存在。这些复杂性包括增强的能力来处理噪音 气味并随着时间的流逝学习以区分非常相似的气味（例如，两种类型的 红酒）。初步证据表明，应该有可能学习 这些复杂性在脊椎动物嗅觉中。研究设计涉及研究 解剖学并记录不同水平的不同类型神经元的发射速率 鼠标嗅觉系统以及应用具有的计算方法和算法 事实证明，在描述这些复杂性的早期工作中被证明是成功的。 第二个主要目标是利用这些大脑区域如何运作以改善的见解 计算机算法的功能。长期以来，许多神经科学家和计算机的梦想 科学家一直在了解大脑的运作方式，以便我们可以翻译见解 从大脑改善机器计算。确实，经验表明大脑 已经进化了计算机使用的信息处理算法的新型变化 科学家解决一般计算问题。有足够的了解算法 这些见解可能会提供提高功能计算机的意外方法 科学算法。此外，了解涉及的电路机制 在嗅觉加工中可以帮助阐明各种气味障碍的基础，并且可能 将来会导致人造气味设备的构建。