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

项目摘要 大规模真实感神经网络模型是研究神经动力学和认知的有力工具 功能协调发展的它整合了通过不同假设确定的多尺度神经生物学机制/过程 和实验数据整合到一个平台上。然而，由于其高度的复杂性和缺乏神经元到神经元的 对应于实验数据，很难使用大的 规模神经活动记录从行为动物，这是最相关的认知过程。我们 建议开发一种受生成对抗网络（GAN）启发的新型建模范式， 协同地联合收割机结合机械和输入-输出（机器学习）建模技术， 缩放现实模型，在功能上与实际的神经元网络无法区分。我们将应用此 范式的海马体的建模，以揭示空间信息是如何编码和重新编码， 海马神经元网络在导航过程中。具体来说，全面的机械模型， 海马将被构建为生成模型，以模拟海马回路如何生成 在导航期间响应于2D轨迹的整体尖峰活动。大规模人口级输入- 将开发输出模型以统计地表征真实的海马体的输入-输出特性， 海马机制模型。机械模型的投入产出模型将由一个 与真实的海马体的真实输入输出模型进行比较。这就形成了歧视性 模型，该模型（1）识别机械模型和真实的海马体之间的差异，以及（2）指导 神经元模型和生成模型的连接参数的优化/修改。这 程序将反复执行，直到机器人无法区分生成（机械） 来自真实的海马体的模型。此外，对由此产生的机械模型的修改 范例将提供可证伪的预测，可以进一步实验测试。我们希望用这个 结合机制和投入产出建模策略，揭示（1）尖峰之间的因果关系 测定了海马不同亚区神经元的活动和（2）海马神经元的位置场 多尺度神经生物学机制以及这些机制之间的相互作用。拟议 方法学将提供一个通用的计算框架，用于整合生物学知识，假设， 和大规模的输入输出数据，以获得更深入和更量化的认知功能的理解。

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.