Neural Population Coding in the Brain

本项目探讨如何组织神经回路的信息处理，以有效地利用大脑资源。提出的理论研究从根本上应用了新的方法来分析皮层地图的组织，通过调查新兴的有效设计原则如何与中央大脑采用的计算机制相关联。其中一个目标是研究如何组织海马体中的“位置地图”（单个细胞在环境的特定位置被调节），以有效地支持一般的目标导向导航。第二个目标是研究在自然视觉场景中形状分布的情况下，如何组织颞下皮层的“形状图”（单个细胞被调谐以响应特定的视觉形状）来有效地支持形状感知。这些理论研究将导致对皮质区IT和海马体调谐曲线分布的直接可测试的预测。这样，该理论将为进一步实验探索大脑的形式视觉和空间导航结构提供一个杠杆。人口密码也涉及不同神经元之间的相互作用，但技术尚未全面研究这些相互作用在皮层。因此，第三个目标是使用视网膜（中央大脑的一部分，已经投射到眼睛中）作为模型系统，从理论上和实验上提出两个基本的，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声仅仅是对相关活动的误读吗？通过询问和回答这些问题，该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。了解视网膜回路对自然和合成刺激的反应将有助于设计有效的假体装置。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。学生和博士后如果能熟练掌握生物和定量物理技术，将能更好地应对未来几十年的科学和工业挑战。该提案的教育部分还通过开发“理论和计算神经科学”课程的教学材料，直接解决了这一国家需求。PI将向小学八年级和高中学生以及公众发表演讲，以扩大公众对该领域的认识。向历史上处于不利地位的社区伸出援手将通过宾夕法尼亚大学现有的项目进行。最后，PI积极组织系列讲座和会议，邀请物理学家在定量系统神经科学领域工作。

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.