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，进一步了解复杂的神经系统，如海马体和其他构成认知和更高思维过程的皮质区域，将取决于数学建模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，用于基于实验的神经元和神经系统的数学建模。这种方法导致了通常所说的“非参数”或“投入-产出”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--而不一定描述内部组件本身。相反，“参数模型”代表了系统的机械属性，其参数可以直接相对于这些基本机制进行解释。我们将在之前工作中继续的谷氨酸能突触模型(EONS)和一个新项目的背景下探索海马体的参数建模：一个大规模的海马区隔神经元模型(10[6]个神经元，10[10]个突触)，它包含了该结构可用的大部分定量神经解剖学、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，建立协同使用非参数和参数建模方法的方法，以加深我们对全球系统动力学如何从分子和突触机制衍生认知，特别是记忆的理解。

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.