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Neural Basis of Causal Inference: Representations, Circuits, and Dynamics

因果推理的神经基础：表示、电路和动力学

基本信息

批准号：
10615006
负责人：
GREGORY C DEANGELIS
金额：
$ 235.04万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10615006
关键词：
Administrative Coordination Advisory Committees Animals Area Back Bayesian Modeling Behavior Behavioral Belief Biological Models Brain Brain region Chemicals Code Communication Communities Data Data Science Data Science Core Decision Making Disease Event Experimental Designs Feedback Foundations Functional disorder Goals Institution Location Macaca Maps Mediating Modeling Monkeys Motion Mus Neural Pathways Neurons Parietal Pattern Perception Physics Population Positioning Attribute Prefrontal Cortex Procedures Process Reporting Research Research Support Retina Role Schizophrenia Sensory Signal Transduction Structure Testing Theoretical Studies Time Training United States National Institutes of Health Update Work autism spectrum disorder control theory data analysis pipeline data sharing data standards density experimental study neural neural circuit neural correlate neural patterning neuromechanism object motion object perception optic flow optogenetics predictive modeling programs response sensorimotor system sensory input synergism theories tool

项目摘要

Project Summary The same pattern of neural activity can correspond to multiple events in the world. Signals sweeping across the retina, for instance, might be generated by a moving object or by the animal's self-motion. The brain resolves this ambiguity by inferring what events best explain sensory activity. This process, called causal inference, is a foundation of action-perception loops in all sensory-motor systems. To support adaptive action, neural representations of variables involved in these computations should be internally consistent. Yet little is known about how such internal models arise, evolve, and interact. This proposal focuses on the neural representations, circuits, and dynamics underlying causal inference in perception of object motion and depth during self-motion. Because the relationships among these variables are defined by physics, not arbitrary trained associations, and because they are likely represented by different cortical areas, the project will be able to study how intercortical connections communicate to maintain an internally consistent view of reality. The overall hypothesis is that causal inference involves computations in parietal and/or prefrontal cortex, and the resulting signals are fed back to sensory areas to update neural representations of task-related variables. Project A will use Bayesian modeling to develop the theoretical framework for studying causal inference in traditional trial-based tasks, and then combine this approach with real-time rational control theory to model continuous, dynamic tasks. These models will be used to fit behavioral data and generate quantitative predictions to compare with behavioral and neural responses in Projects B and C. Using trial-based tasks in monkeys, Project B will ask how causal inference modulates neural correlates of flow parsing (in which background motion influences perception of object motion), will examine how sensory representations are updated by causal inference about object motion, and will use chemical and optogenetic inactivation to identify the specific neural pathways that are necessary for such updating of sensory representations. In naturalistic, continuous navigation tasks, Project C will use similar recording and neural manipulation approaches to examine the neural dynamics of causal inference in monkeys, and will map neural correlates of dynamic causal inference across the entire mouse brain in high-density neural recordings. The Data Science Core will formalize procedures for storing and sharing data, and develop a standard data-processing pipeline, while the Administrative Core will coordinate among the team and manage internal and external advisory committees. These comprehensive research efforts are expected to identify direct correlates of causal inference in single neurons and neural populations and determine how the resulting beliefs about states of the world are propagated from decision-making regions back to sensory regions of the brain. Successful completion of this work will illuminate the functional roles of feedback projections and neural coding in sensory areas of the brain, move the field toward naturalistic continuous behavior, and help close the loop between perception and action.

项目摘要相同的神经活动模式可以对应世界上的多个事件。信号横扫例如，视网膜可能是由运动的物体或动物的自我运动产生的。大脑通过推断什么事件最能解释感觉活动来解决这种模糊性。这个过程，叫做因果关系，推理是所有感觉运动系统中动作感知回路的基础。为了支持自适应动作，这些计算中涉及的变量的神经表征应该是内部一致的。但很少有人了解这些内部模型是如何产生、发展和相互作用的。该建议侧重于神经对物体运动和深度的感知中潜在的因果推理的表征、回路和动力学在自我运动中。因为这些变量之间的关系是由物理学定义的，而不是任意的训练的关联，因为它们可能由不同的皮层区域代表，该项目将能够来研究皮层间的联系是如何沟通的，以保持对现实的内部一致的看法。的总的假设是，因果推理涉及顶叶和/或前额叶皮层的计算，得到的信号被反馈到感觉区域以更新任务相关变量的神经表征。项目A将使用贝叶斯建模来开发研究因果推理的理论框架，传统的基于试验的任务，然后联合收割机结合这种方法与实时理性控制理论建模连续的、动态的任务。这些模型将用于拟合行为数据并生成定量的预测与项目B和C中的行为和神经反应进行比较。使用基于试验的任务猴子，项目B将询问因果推理如何调节流解析的神经相关性（其中背景运动影响物体运动的感知），将研究感官表征是如何通过关于物体运动的因果推理更新，并将使用化学和光遗传失活来识别这些特定的神经通路是更新感觉表征所必需的。在自然主义中，连续导航任务，项目C将使用类似的记录和神经操作方法，研究猴子因果推理的神经动力学，并将绘制动态因果推理的神经相关图。在高密度神经记录中，因果推理贯穿整个小鼠大脑。数据科学核心将正式确定存储和共享数据的程序，并开发标准的数据处理管道，行政核心将在团队之间进行协调，并管理内部和外部咨询委员会。这些全面的研究工作，预计将确定直接相关的因果推理，在单一的神经元和神经群体，并决定如何产生的信念，对世界的状态是从大脑的决策区域传播到感觉区域。成功完成本工作将阐明反馈投射和神经编码在大脑感觉区域中的功能作用，将场移向自然主义的连续行为，并帮助闭合感知和行动之间的循环。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Aberrant causal inference and presence of a compensatory mechanism in autism spectrum disorder.

DOI：
10.7554/elife.71866
发表时间：
2022-05-17
期刊：
ELIFE
影响因子：
7.7
作者：
Noel, Jean-Paul;Shivkumar, Sabyasachi;Dokka, Kalpana;Haefner, Ralf M.;Angelaki, Dora E.
通讯作者：
Angelaki, Dora E.

Causal inference during closed-loop navigation: parsing of self- and object-motion.

闭环导航期间的因果推理：自运动和物体运动的解析。

DOI：
10.1101/2023.01.27.525974
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Noel,Jean-Paul;Bill,Johannes;Ding,Haoran;Vastola,John;DeAngelis,GregoryC;Angelaki,DoraE;Drugowitsch,Jan
通讯作者：
Drugowitsch,Jan

Eye movements reveal spatiotemporal dynamics of visually-informed planning in navigation.

DOI：
10.7554/elife.73097
发表时间：
2022-05-03
期刊：
ELIFE
影响因子：
7.7
作者：
Zhu, Seren;Lakshminarasimhan, Kaushik J.;Arfaei, Nastaran;Angelaki, Dora E.;Zhang, Hang
通讯作者：
Zhang, Hang

Cognitive, Systems, and Computational Neurosciences of the Self in Motion.