Combined Mechanistic and Input-Output Modeling of the Hippocampus During Spatial Navigation

空间导航过程中海马的机械和输入输出组合建模

基本信息

批准号：
10263699
负责人：
Dong Song
金额：
$ 119.81万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

项目摘要

PROJECT ABSTRACT Large-scale realistic model of neuronal network is a powerful tool for studying neural dynamics and cognitive functions. It integrates multi-scale neurobiological mechanisms/processes identified through diverse hypotheses and experimental data into a single platform. However, due to its high complexity and lack of neuron-to-neuron correspondence to experimental data, it is difficult to constrain, validate and optimize such a model using large- scale neural activities recorded from behaving animals, which are most relevant to cognitive processes. We propose to develop a novel modeling paradigm inspired by the generative adversarial network (GAN) to synergistically combine both mechanistic and input-output (machine learning) modeling techniques to build large- scale realistic models that are functionally indistinguishable from actual neuronal networks. We will apply this paradigm to the modeling of the hippocampus to reveal how spatial information is encoded and re-encoded in the hippocampal neuronal networks during navigation. Specifically, full-scale mechanistic model of the hippocampus will be constructed as the generative model to simulate how hippocampal circuits generate ensemble spiking activities in response to 2D trajectories during navigation. Large-scale population-level input- output models will be developed to statistically characterize input-output properties of the real hippocampus and the hippocampal mechanistic model. The input-output models of the mechanistic model will be evaluated by a discriminator against the ground truth input-output models of the real hippocampus. This forms the discriminative model that (1) identifies discrepancies between the mechanistic model and the real hippocampus, and (2) guides the optimization/modification of neuron model and connectivity parameters of the generative model. This procedure will be performed iteratively until the discriminator fails to distinguish the generative (mechanistic) model from the real hippocampus. In addition, the modifications to the mechanistic model generated by this paradigm will provide falsifiable predictions that can be further tested experimentally. We expect to use this combined mechanistic and input-output modeling strategy to unveil how (1) causal relations between spiking activities across different hippocampal subregions, and (2) place fields of hippocampal neurons, are determined by multi-scale neurobiological mechanisms and the interplay between these mechanisms. The proposed methodology will provide a general computational framework for integrating biological knowledge, hypotheses, and large-scale input-output data to gain deeper and more quantitative understanding of cognitive functions.

项目摘要神经元网络的大规模现实模型是研究神经动力学和认知的强大工具功能。它整合了通过各种假设确定的多尺度神经生物学机制/过程和实验数据到一个平台。但是，由于其高复杂性和缺乏神经元到神经元与实验数据的对应关系，很难使用大型限制，验证和优化这种模型从行为动物中记录的刻度神经活动，这与认知过程最相关。我们建议开发一种新型的建模范式，灵感来自生成对抗网络（GAN）协同结合机械和输入输出（机器学习）建模技术，以构建大型比例现实的模型在功能上与实际神经元网络无法区分。我们将应用此与海马建模的范式揭示了如何对空间信息进行编码和重新编码导航期间海马神经元网络。具体而言，全尺度机械模型海马将被构造为模拟海马电路的生成模型在导航过程中响应2D轨迹的合奏尖峰活动。大规模的人口级输入 - 将开发输出模型以统计表征真实海马的输入输出属性和海马机械模型。机械模型的输入输出模型将通过反对真实海马的地面真相输出模型的歧视者。这形成了歧视性（1）识别机械模型和实际海马之间的差异，以及（2）指南神经元模型和生成模型的连接参数的优化/修改。这过程将进行迭代执行，直到歧视器无法区分生成（机械）来自真实海马的模型。另外，对此产生的机械模型的修改范式将提供可伪造的预测，可以通过实验进行进一步测试。我们希望使用这个结合机械和投入输出建模策略，以揭示（1）峰值之间的因果关系确定了不同海马子区域的活动，以及（2）海马神经元的位置场通过多尺度的神经生物学机制和这些机制之间的相互作用。提议方法论将提供一个一般的计算框架，用于整合生物学知识，假设，和大规模输入数据，以获得对认知功能的更深入，更定量的理解。