Multi-Scale Modeling of Hippocampal Dynamics and Neural Prostheses

生物医学工程，特别是当它应用于神经科学时，已经达到了一个发展阶段，在这个阶段，对复杂神经系统的进一步理解，如海马体和其他皮层区域，是认知和高级思维过程的基础，将依赖于数学建模作为组织已知实验数据的手段，并系统地探索未知。核心项目#4的研究目标是进一步发展和应用基于非线性系统理论原理的方法，以实验为基础，神经元和神经系统的数学建模。这种方法导致通常被称为“非参数”或“输入-输出”模型，即，作为系统内部组件之间相互作用的结果而出现的功能属性-无需描述内部组件本身。相反，“参数化模型”表示系统的机械特性，其参数可以根据那些潜在的机制直接解释。我们将在谷氨酸能突触模型（EONS）和一个新项目的背景下探索海马的参数化建模：一个大规模的海马体区室神经元模型（10[6]神经元，10[10]突触），该模型结合了该结构的许多定量神经解剖学、突触生理学和地形连接。最终，我们的目标是在加速多尺度建模的背景下，建立非参数和参数建模方法的协同使用手段，以进一步了解全局系统动力学如何导致认知，特别是记忆，源自分子和突触机制。

Biomedical engineering, particularly as it applies to neuroscience, has reached a stage of development at which further understanding of complex neural systems, such as the hippocampus and other cortical regions that underlie cognition and higher thought processes, will depend on mathematical modeling as a means to organize experimental data that is known, and to systematically explore the unknown. The research objectives of Core Project #4 are to further develop and apply methodologies based on principles of nonlinear systems theory for experimentally-based, mathematical modeling of neurons and neural systems. This approach leads to what are commonly termed "non-parametric" or "input-output" models, i.e., functional properties that emerge as a consequence of interactions among the internal components of the system - without necessarily describing the internal components themselves. In contrast, "parametric models" represent the mechanistic properties of the system, with parameters that can be interpreted directly with respect to those underlying mechanisms. We will explore parametric modeling of the hippocampus both in the context of a glutamatergic synaptic model (EONS) continued from previous work, and a new project: a large-scale, compartmental neuron model (10[6] neurons, 10[10] synapses) of hippocampus that incorporates much of the quantitative neuroanatomy, synaptic physiology, and topographic connectivity available for that structure. Ultimately our goal is to establish means for the synergistic use of non-parametric and parametric modeling methods, in the context of accelerating multi-scale modeling, to further our understanding of how global system dynamics underlying cognition, and specifically memory, derive from molecular and synaptic mechanisms.