Full-scale biophysical modeling of hippocampal networks during spatial navigation and memory replay

在我们对海马体记忆重演潜在机制的研究中，我们将 在实验方法和建模方法之间采用持续交互的策略。第一, 我们考虑概念模型，并提出与启动、生成和 终止SPW-R。然后，当我们收集关键的新实验数据来测试这些假设时， 项目1-4，我们将使用这些数据来约束完整的生物物理细节计算模型 将新开发的CA3模型与现有的齿状回模型相结合 脑回(DG)和CA1。然后，这些模型将用于预测网络的复杂响应 模拟扰动，帮助解释实验结果，告知假设潜在的 原理，并建议进行新的实验来检验这些假设。除了改进的 对内存回放的概念性理解，我们的最终产品将包括 海马体具有前所未有的功能，完全开源，并可免费提供给 更广泛的神经科学界。 好了！

Throughout our investigation of the mechanisms underlying hippocampal memory replay, we will employ a strategy of continuous interaction between experimental and modeling approaches. First, we consider conceptual models, and formulate hypotheses relevant to the initiation, generation and termination of SPW-Rs. Then, as we gather critical new experimental data to test those hypotheses in Projects 1-4, we will use that data to constrain full-scale biophysically-detailed computational models of hippocampal networks, integrating a newly developed model of CA3 with existing models of dentate gyrus (DG) and CA1. These models will then be used to predict the complex responses of the network to simulated perturbations, help interpret experimental outcomes, inform hypotheses about underlying principles, and suggest new experiments to test those hypotheses. In addition to an improved conceptual understanding of memory replay, our final product will include full-scale network models of the hippocampus with unprecedented functionality, fully open-sourced and freely available to the broader neuroscience community. !