Multi-Scale Modeling of Hippocampal Dynamics and Neural Prostheses

生物医学工程，特别是应用于神经科学的生物医学工程，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，如海马和其他认知和高级思维过程的基础皮层区域，将依赖于数学建模作为组织已知实验数据和系统地探索未知的手段。核心项目#4的研究目标是进一步开发和应用基于非线性系统理论原理的方法，用于神经元和神经系统的实验性数学建模。这种方法导致通常称为“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-不一定描述内部组件本身。相比之下，“参数模型”代表系统的机械特性，其参数可以直接相对于那些潜在的机制进行解释。我们将探索海马体的参数化建模，这两个模型都是在从以前的工作中延续下来的神经元突触模型（EONS）的背景下进行的，以及一个新的项目：海马体的大规模房室神经元模型（10[6]个神经元，10[10]个突触），该模型结合了该结构的大量定量神经解剖学，突触生理学和拓扑连接性。最终，我们的目标是建立协同使用非参数和参数建模方法的方法，在加速多尺度建模的背景下，进一步了解全球系统动力学的认知，特别是记忆，来自分子和突触机制。

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.