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，已经达到了一个发展阶段，在这个阶段，对复杂神经系统（例如海马体和其他构成认知和高级思维过程的皮质区域）的进一步理解将依赖于数学模型作为组织已知实验数据和系统探索未知事物的手段。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，对神经元和神经系统进行基于实验的数学建模。这种方法产生了通常所说的“非参数”或“输入输出”模型，即由于系统内部组件之间相互作用而出现的功能属性 - 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，其参数可以根据这些基础机制直接解释。我们将在从之前的工作中延续的谷氨酸突触模型（EONS）和一个新项目的背景下探索海马的参数化建模：海马的大规模、区室神经元模型（10[6]个神经元，10[10]个突触），其中结合了该结构可用的大量定量神经解剖学、突触生理学和拓扑连接。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协同使用的方法，以进一步了解认知（特别是记忆）背后的全局系统动力学如何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.