Clustering of Neural Activity: A Design Principle for Population Codes

神经活动的聚类：群体代码的设计原则

• 批准号: 1806932
• 负责人: Michael Berry
• 金额: $ 45万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1806932&HistoricalAwards=false
• 关键词: Clustering; Neural; Activity; Design; Principle

In virtually every part of the brain, information about the sensory environment, internal body states, or intended movements is encoded by more than one neuron. This was apparent as early as the nineteenth century from the extensive interconnectivity of nearby neurons and continues to be apparent from numerous measurements of the tuning curves and correlation of nearby neurons. Despite its fundamental importance, population neural codes are poorly understood. In this project the PI will test a recently developed principle for population code - namely, that neural activity patterns should always be organized into a discrete set of clusters. This organization is appealing, because clusters exhibit error correction, encode qualitatively different stimulus features than their constituent neurons, and can be learned in an unsupervised fashion by downstream neural circuits. Together, these properties enable a powerful form of hierarchical detection of complex stimulus features. The PI aims at developing a principle of population coding that applies widely across the brain. Furthermore, his hypothesis is connected to a model of hierarchical feature detection, which may be operating across all the ascending pathways in the neocortex. In addition, the neocortex's modular structure lends itself to machine learning algorithms. In addition,the project will involve the development and dissemination of software to perform fits of maximum entropy and hidden Markov models to neural data, which could help spur on the research programs of many labs that use these methods to analyze neural populations.This proposal will combine large-scale neural recording methods from the retina and cortex with state-of-the-art theoretical analyses to study collective phenomena in population neural codes. The PI has an extensive track record in multi-electrode recording from the vertebrate retina, along with applying maximum entropy models to analyze states of network activity. In addition, the PI has recently started a collaboration with Prof. David Tank to record from cortical populations using two-photon calcium fluorescence imaging. The project's aims are: 1. Study how cluster codes can simultaneously encode categorical and continuous stimulus information; if true, this idea would constitute a new example of multiplexed coding in neural populations 2. Study how biologically plausible neural networks can learn and readout clusters; if true, then this makes possible hierarchical cluster processing in the neocortex and will lead us to formulate specific experimental tests to see if layer 4 of V1 is learning LGN clusters. 3. Test whether cluster codes are present in central brain areas, like the visual cortex and hippocampus; this study may generalize his results beyond the retina and help establish a new design principle for population codes. This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Modulation Program in the Division of Integrative Organismal Systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

实际上，在大脑的每个部分，有关感觉环境、身体内部状态或预期运动的信息都是由多个神经元编码的。这一点早在19世纪就从附近神经元的广泛互联性中得到了明显的体现，并且从对附近神经元的调谐曲线和相关性的大量测量中得到了明显的体现。尽管它具有重要的基础意义，但人们对群体神经密码的理解却很少。在这个项目中，PI将测试最近开发的人口代码原则，即神经活动模式应该总是被组织成一组离散的簇。这种组织是有吸引力的，因为集群表现出错误纠正，编码的刺激特征与组成它们的神经元的性质不同，并且可以通过下游神经回路以无监督的方式学习。总之，这些特性使复杂刺激特征的分层检测成为一种强大的形式。PI旨在开发一种广泛应用于整个大脑的种群编码原理。此外，他的假设与分层特征检测模型相关联，该模型可能在新皮层的所有上行通路上运行。此外，新大脑皮层的模块化结构有助于机器学习算法。此外，该项目将涉及软件的开发和传播，以执行最大熵和隐马尔可夫模型对神经数据的拟合，这可能有助于推动许多实验室使用这些方法来分析神经种群的研究项目。该提案将结合视网膜和皮层的大规模神经记录方法与最先进的理论分析来研究群体神经编码中的集体现象。PI在脊椎动物视网膜的多电极记录方面有着广泛的记录，同时还应用最大熵模型来分析网络活动状态。此外，PI最近开始与David Tank教授合作，使用双光子钙荧光成像记录皮质种群。该项目的目标是：1；研究簇码如何同时编码分类和连续刺激信息；如果这是真的，这个想法将构成神经群体中多路编码的一个新例子。研究生物学上合理的神经网络如何学习和读出集群；如果这是真的，那么这使得在新皮层中进行分层簇处理成为可能，并将引导我们制定具体的实验测试，以查看V1的第4层是否正在学习LGN簇。3. 测试簇码是否存在于大脑中央区域，如视觉皮层和海马体；这项研究可能将他的结果推广到视网膜之外，并有助于建立一个新的人口编码设计原则。该项目由物理系生命系统物理学项目和综合有机系统部调制项目联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Clustering of Neural Activity: A Design Principle for Population Codes

• DOI: 10.3389/fncom.2020.00020
• 发表时间: 2020-03-13
• 期刊: FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
• 影响因子: 3.2
• 作者: Berry, Michael J., II;Tkacik, Gasper
• 通讯作者: Tkacik, Gasper

Passive Exposure Sparsifies Neural Activity in the Primary Visual Cortex

被动暴露会使初级视觉皮层的神经活动稀疏

• DOI: 10.1101/2021.11.18.469160
• 发表时间: 2021
• 期刊: bioRxiv
• 作者: Jan Homann, Hyewon Kim