Neural Population Coding in the Brain

这个项目探讨了如何通过神经回路组织信息处理，以有效地利用大脑的资源。 所提出的理论研究从根本上应用新的方法来分析皮层地图的组织，通过调查新兴的高效设计原则如何适用于中央大脑所采用的计算机制。其中一个目标是研究海马体中的“位置图”（单个细胞在环境的特定位置被调整以激发）应该如何组织，以有效地支持一般目标导向的导航。 第二个目标是研究如何组织下颞叶皮层的“形状图”（其中单个细胞被调整为响应特定的视觉形状），以有效地支持形状感知，给定自然视觉场景中的形状分布。 这些理论研究将导致直接可测试的预测的皮质区IT和海马的调谐曲线的分布。这样，该理论将为进一步的实验探索大脑中的形状视觉和空间导航的结构提供一个杠杆。 群体编码也涉及不同神经元之间的相互作用，但目前还没有技术来全面研究皮层中的这些相互作用。 因此，第三个目标使用视网膜（投射到眼睛中的中央大脑的一部分）作为模型系统，从理论上和实验上提出两个基本的，但尚未回答的问题：（a）神经网络是否像优化理论预测的那样，使它们的相互作用适应刺激统计和噪声？(b)从单个神经元测量的噪音，仅仅是对相关活动的误读吗？ 通过询问和回答这些问题，本项目还将解释视网膜如何为中央处理准备视觉输入的关键方面。 视网膜回路如何对自然和合成刺激作出反应的知识将有助于设计有效的修复装置。该项目通过将物理学和机器学习的分析和理论方法与神经科学的实验技术结合起来，加强了学科之间的研究联系。 因此，精通生物和定量物理技术的学生和博士后将能够更好地科普未来几十年的科学和工业挑战。 该提案的教育部分还通过为“理论和计算神经科学”课程编写教材，直接满足了这一国家需求。 PI将向K-8和高中学生以及公众进行演讲，以扩大公众对该领域的了解。 对历史上处于不利地位的社区的宣传将通过宾夕法尼亚大学的既定方案进行。 最后，PI积极组织系列讲座和会议，吸引物理学家在定量系统神经科学中工作。

This project explores how information processing by neural circuits is organized to use the resources of the brain efficiently. The proposed theoretical studies apply fundamentally new approaches to analyzing the organization of cortical maps, by investigating how emerging principles of efficient design pertain to the computational mechanisms employed by the central brain. One aim studies how the "place map" in the hippocampus (where individual cells are tuned to fire in particular locations of an environment) should be organized to efficiently support general goal-directed navigation. A second aim studies how the "shape map" in Inferotemporal Cortex (where individual cells are tuned to fire in response to particular visual shapes) should be organized to efficiently support shape perception, given the distribution of shapes in natural visual scenes. These theoretical studies will lead to directly testable predictions of the distribution of tuning curves in cortical area IT and hippocampus. In this way, the theory will provide a lever for further experimental exploration of the architecture of form vision and spatial navigation in the brain. Population codes also involve interactions between the different neurons, but techniques are not yet available to comprehensively study these interactions in cortex. Thus, a third aim uses the retina (a piece of the central brain that has projected out into the eye) as a model system to theoretically and experimentally ask two basic, and as yet unanswered questions: (a) Do neural networks adapt their interactions to stimulus statistics and noise as predicted by optimization theory?; (b) Is noise, as measured from single neurons, simply a mis-reading of correlated activity? By asking and answering these questions, this project will also explain key aspects of how the retina prepares visual input for central processing. Knowledge of how retinal circuits respond to natural and synthetic stimuli will be useful in designing effective prosthetic devices. This project strengthens the research connections between disciplines by bringing together analytical and theoretical methods from physics and machine learning with experimental techniques from neuroscience. Students and postdocs who thus develop proficiency with both biological and quantitative physical techniques will be better able to cope with scientific and industrial challenges of coming decades. The educational component of this proposal also addresses this national need directly by developing pedagogical materials for a course on "Theoretical and Computational Neuroscience". The PI will give presentations to K-8 and high school students and to the general public with a view to broadening public knowledge of the field. Outreach to historically disadvantaged communities will be carried out through established programs at Penn. Finally, the PI is active in organizing lecture series and conferences that engage physicists to work within quantitative systems neuroscience.