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

项目摘要 大规模真实的神经网络模型是研究神经动力学和认知的有力工具 功能。它集成了通过不同假说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中。然而，由于其高度的复杂性和缺乏神经元到神经元 与实验数据相对应，很难使用大量的数据来约束、验证和优化这样的模型。 从动物的行为中记录下来的神经活动的规模，这与认知过程最相关。我们 建议在生成性对抗网络(GAN)的启发下开发一种新的建模范式 协同结合机械性和输入-输出(机器学习)建模技术，以构建大型 缩放在功能上与实际神经元网络无法区分的逼真模型。我们将应用这一点 海马体建模的范例，以揭示空间信息是如何编码和重新编码的 导航过程中的海马神经元网络。具体地说，全尺寸的机械模型 海马体将被构建为生成模型，以模拟海马体电路是如何生成的 在导航过程中响应2D轨迹的整体尖峰活动。大规模人口层面投入-- 将开发输出模型来统计描述真实海马体的输入-输出特性和 海马区的机械模型。机械模型的投入产出模型将通过一个 针对真实海马体的基本事实输入输出模型的鉴别器。这就形成了歧视性的 模型，(1)确定机械模型和真实海马体之间的差异，以及(2)引导 神经元模型和产生式模型的连接性参数的优化/修改。这 程序将反复执行，直到鉴别器无法区分生成的(机械的) 来自真实海马体的模型。此外，由此产生的对机械模型的修改 范型将提供可证伪的预测，这些预测可以通过实验进一步检验。我们希望利用这一点 结合机制和投入产出建模策略，揭示(1)尖峰现象之间的因果关系 测定了不同海马亚区的活动，以及(2)海马神经元的位置场 通过多尺度神经生物学机制以及这些机制之间的相互作用。建议数 方法论将提供一个通用的计算框架，用于整合生物学知识、假说、 以及大规模的投入产出数据，以获得对认知功能的更深层次和更定量的理解。

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.