COMPUTATIONAL SYMBOLIC MODELS OF NEURAL NETWORK DYNAMICS

这项研究的长期目标是产生一个动态的，象征性的， 一个功能生物神经网络的计算机模拟。 的 目的是使用模拟作为研究工具， 影响神经系统稳健性、可塑性、健康和 疾病 模拟将基于数学和物理 对实际神经结构和电生理学的解释， 哺乳动物的神经网络，前庭黄斑，其中有许多 更先进的系统。 拟议的调查将填补 我们目前对网络之间关系的认识差距 几何和神经编码，并将改善现有的模拟， 使其成为更好的研究工具。 本研究的具体目的是 1)识别前庭电生理学不同类别 沙鼠的传入神经，并在细胞内用辣根标记它们 过氧化物酶（HRP），2）重建标记的神经/终末感受性 场，突触，传出终末和周围部分的神经 网络作为三维立体图像，3）评估对应性 前庭神经几何结构之间的联系， 使用房室模型的突触学，4）开发算法以改进 一个当前的模拟，以便它更接近地模仿前庭网络 动力学 我们提出了一个十室模型， 强调突触区和分支汇合的明确边界。 的 分区模型将被纳入一个象征性的，动态的 模拟，将用于确定如何神经几何和 即时网络环境影响神经放电模式。 一些 实验将测试可能构成系统基础的特性， 鲁棒性，例如布线中的冗余和受约束的随机性; 其他人将探索内在前馈反馈回路的影响 和神经放电模式的外在突触前调制， 编码 该模型可以很容易地操作，以研究的贡献， 网络动态、健康和疾病的组成部分。 仿真 直接适用于研究一些内耳疾病， 平衡失调，如梅尼埃综合征，或研究 耳毒性药物，通过选择性遗漏的组成部分，如I型头发 细胞 该模拟作为一种研究模型具有更广泛的意义 更先进的神经网络的正常和异常功能。 许多 黄斑神经网络的特征被更复杂的 生物系统：例如，处理元件的非模块化， 前馈反馈回路、分段、突触加权，以及 相互突触 相关工作将应用非线性动力系统 理论到电生理测试期间记录的神经脉冲串， 为了了解混沌在神经健康和神经系统中的作用， 可塑性

The long-term goal of this research is to produce a dynamic, symbolic, computer simulation of a functioning biological neural network. The purpose is to use the simulation as an investigative tool for studying factors contributing to neural system robustness, plasticity, health and disease. The simulation will be based on mathematical and physical interpretations of actual neural architecture and electrophysiology in a mammalian neural network, the vestibular macula, which has many of the attributes of more advanced systems. The proposed investigation will fill the present gap in our knowledge of the relationship between network geometry and neural coding and will improve upon an existing simulation, making it a better research tool. The specific aims of this study are to 1) identify electrophysiologically distinct classes of vestibular afferents in gerbils and to label them intracellularly with horseradish peroxidase (HRP), 2) reconstruct the labeled nerve/terminal receptive fields, synapses, efferent boutons, and surrounding portions of the neural network as three dimensional solid images, 3) evaluate the correspondence between functional clailsification and vestibular neural geometry and synaptology using a compartmental model, 4) develop algorithms to improve a current simulation so that it more closely mimics vestibular network dynamics. We propose a ten-compartment model that has anatomically defined boundaries emphasizing synaptic zones and branch confluences. The compartmental model will be incorporated into a symbolic, dynamic simulation that will be used to determine how neural geometry and the immediate network environment influence nerve discharge patterns. Some experiments will test for properties that might underlie system robustness, such as redundancy and constrained randomness in wiring; others will explore the influence of intrinsic feedforward-feedback loops and of extrinsic, presynaptic modulation on neural discharge patterns and coding. The model can be easily manipulated to study the contributions of component parts to network dynamics, health and disease. The simulation is directly applicable to the study of some inner ear disorders resulting in dysequilibrium, such as Meniere's syndrome, or the study of the effects of ototoxic drugs, by selective omission of components such as type I hair cells. The simulation has broader significance as a model for studying normal and abnormal functioning of more advanced neural networks. Many features of the macular neural network are shared by more complex biological systems: for example, non-modularity of processing elements, feedforward-feedback loops, segmentation, synaptic weighting, and reciprocal synapses. Related work will apply nonlinear dynamical systems theory to nerve pulse trains recorded during electrophysiological testing, in order to learn the role of chaos in neural health and in neural plasticity.