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CRCNS: Neural coding and computation in large ensembles in prefrontal cortex

CRCNS：前额皮质大型集合中的神经编码和计算

基本信息

批准号：
9487337
负责人：
Roozbeh Kiani
金额：
$ 23.67万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9487337
关键词：
Address Alzheimer&apos s Disease Architecture Area Behavior Biological Neural Networks Brain Cells Code Cognition Cognitive Complex Computer Simulation Data Data Analyses Data Set Decision Making Dependence Dimensions Electrodes Electrophysiology (science)Entropy Experimental Models Family Future Goals Graph Heart Impairment Individual Intuition Light Literature Macaca Machine Learning Maps Measures Mental disorders Modeling Monkeys Nature Network-based Neurons Neurosciences Noise Optics Parkinson Disease Pathology Pattern Population Population Dynamics Prefrontal Cortex Primates Process Psychological Models Reporting Retina Role Schizophrenia Shapes Source Statistical Data Interpretation Statistical Models Stimulus Structure System Testing Validation Variant Vocabulary Work association cortex base behavioral outcome behavioral pharmacology cognitive ability cognitive process computer framework design experimental study high dimensionality insight mental function nervous system disorder neural circuit neuromechanism neurophysiology next generation predictive modeling relating to nervous system response sensory cortex spatiotemporal tool

项目摘要

PROJECT SUMMARY The essence of cognition is choice, and to understand choice we need to understand the brain mechanisms that guide decisions in complex settings. These mechanisms are implemented through interactions of large neural networks across cortical and subcortical areas. Tracking population response dynamics on single trials and relating them to internal cognitive states and overt behavior are critical for incisive tests of current models of decision-making. Here we propose to characterize the activity patterns of large populations of neurons (100+) in the prefrontal cortex of macaque monkeys engaged in decision-making tasks. We will explain the spatiotemporal patterns of activation in the population by developing the most parsimonious probabilistic model that takes into account pairwise and higher-order interactions of neurons. The model will be utilized to characterize response manifolds of the network and quantify its dynamics in different task epochs. Our approach is unique because rather than trying to embed the population dynamics in a low dimensional manifold using Machine Learning tools, we propose to extend the Maximum Entropy framework to directly capture the high-dimensional dynamics. Further, by characterizing functional dependencies among cells we can map the architecture and design of large networks in terms of subnetwork motifs and computations. Finally, we will investigate how noisy fluctuations of responses or artificial manipulation of network activity influences its dynamics and modifies or disturbs its computations. Scrutinizing the results of these experiments within our modeling framework makes headway toward addressing long-standing questions about decision-making, including the neural basis of psychological models and effects of initial state on the behavior. Our work will have broader impacts in two domains. First, the path that we will chart for discovering functional subnetworks and their computations will be useable in various subfields of neuroscience. We will significantly advance data analysis and computational modeling tools available to neuroscientists and, therefore, will facilitate future studies of normal mental functions and mental disorders using high-dimensional neural data. Second, characterizing information encoding and response dynamics in the prefrontal cortex sheds light on mechanisms of decision-making and emergence of cognitive abilities in complex neural networks. Deficits of decision-making are at the heart of a number of neurological and psychiatric disorders including schizophrenia, Alzheimer's, and Parkinson's disease. Several behavioral and pharmacological therapies have been proposed for those deficits, but we lack a clear understanding of how they work at the level of neuronal systems. To develop the next generation of therapies, we need to understand how cognitive processes emerge across multiple functional levels, from individual neurons to networks of brain areas. Our work is a step in that direction. It has the potential to advance our understanding of pathology of mental disorders and help with the discovery of better treatments.

项目总结认知的本质是选择，要理解选择，我们需要了解在复杂环境中指导决策的大脑机制。这些机制是通过跨皮质和皮质下区域的大型神经网络的相互作用来实现的。跟踪单个试验的群体反应动态，并将它们与内部认知状态和公开行为联系起来，对于深入测试当前的决策模型至关重要。在这里，我们建议描述参与决策任务的猕猴前额叶皮质中大量神经元(100+)的活动模式。我们将通过发展最简约的概率模型来解释种群中激活的时空模式，该模型考虑了神经元的成对和更高阶的相互作用。该模型将被用来刻画网络的响应流形，并量化其在不同任务时期的动力学。我们的方法是独特的，因为我们不是试图使用机器学习工具将种群动态嵌入到低维流形中，而是建议扩展最大熵框架来直接捕获高维动态。此外，通过表征单元之间的功能依赖关系，我们可以根据子网络主题和计算来映射大型网络的体系结构和设计。最后，我们将调查响应的噪声波动或对网络活动的人为操纵如何影响其动力学，并修改或干扰其计算。在我们的建模框架内仔细审查这些实验的结果，有助于解决关于决策的长期存在的问题，包括心理模型的神经基础和初始状态对行为的影响。我们的工作将在两个领域产生更广泛的影响。首先，我们将绘制的发现功能子网络的路径及其计算将适用于神经科学的各个子领域。我们将大大提高神经科学家可用的数据分析和计算建模工具，因此，将促进使用高维神经数据对正常精神功能和精神障碍的未来研究。第二，表征前额叶皮质的信息编码和反应动力学有助于揭示复杂神经网络中的决策机制和认知能力的出现。决策缺陷是许多神经和精神疾病的核心，包括精神分裂症、阿尔茨海默氏症和帕金森氏症。针对这些缺陷已经提出了几种行为和药物治疗方法，但我们缺乏对它们在神经系统水平上如何发挥作用的清楚了解。为了开发下一代疗法，我们需要了解认知过程是如何跨越多个功能水平出现的，从单个神经元到大脑区域网络。我们的工作就是朝着这个方向迈出的一步。它有可能促进我们对精神障碍病理学的理解，并有助于发现更好的治疗方法。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（1）

Neural Mechanisms That Make Perceptual Decisions Flexible.

DOI：
10.1146/annurev-physiol-031722-024731
发表时间：
2023-02-10
期刊：
Annual review of physiology
影响因子：
18.2
作者：
通讯作者：

Learning probabilistic neural representations with randomly connected circuits.

DOI：
10.1073/pnas.1912804117
发表时间：
2020-10-06
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Maoz O;Tkačik G;Esteki MS;Kiani R;Schneidman E
通讯作者：
Schneidman E

Editorial overview: Neurobiology of cognitive behavior: Complexity of neural computation and cognition.

编辑概述：认知行为的神经生物学：神经计算和认知的复杂性。