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、 计算机模拟正常运行的生物神经网络。这个 目的是将模拟作为研究的工具 影响神经系统健壮性、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，它有许多 更高级系统的属性。拟议的调查将填补 我们目前对网络之间关系的认识上的差距 几何和神经编码，并将在现有模拟的基础上进行改进， 使其成为更好的研究工具。这项研究的具体目的是 1)识别电生理上不同类别的前庭 沙土鼠的传入神经并用辣根对其进行细胞内标记 过氧化物酶(HRP)，2)重建标记的神经/终末感受器 神经的区域、突触、传出神经和周围部分 网络作为三维立体图像，3)评价对应关系 功能克隆和前庭神经几何学之间的关系 使用隔室模型的突触，4)开发算法以改进 当前的模拟，使其更接近于模拟前庭网络 动力学。我们提出了一个十室模型，从解剖学上讲， 明确的边界强调突触区域和分支汇合。这个 车厢模型将被纳入一个象征性的、动态的 模拟将被用来确定神经几何学和 即时网络环境影响神经放电模式。一些人 实验将测试可能构成系统基础的属性 健壮性，如布线中的冗余性和受限随机性； 其他人将探索内在前馈-反馈环路的影响 以及外源性、突触前调制对神经放电模式的影响 编码。可以很容易地操纵该模型来研究 网络动态、健康和疾病的组成部分。该模拟 直接适用于某些内耳疾患的研究 平衡失调，如梅尼埃综合征，或对其影响的研究 对于耳毒性药物，通过选择性地省略成分，如I型毛发 细胞。仿真作为一种研究模型具有更广泛的意义 更高级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-反馈环路、分段、突触权重和 相互突触。相关工作将应用于非线性动力系统 电生理测试期间记录的神经脉冲串的理论， 为了了解混沌在神经健康和神经中的作用 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.