Sanderson A C, Peterka R J
Crit Rev Biomed Eng. 1985;12(3):237-309.
The complexity of the nervous system poses challenging experimental and theoretical problems to the investigator. While experimental studies have provided a broad understanding of the physiological and anatomical role of neural elements, a comprehensive description of how the nervous system acquires and processes information is still lacking. Neural modeling addresses itself to the quantitative interpretation of neurophysiological experiments, to the assimilation of diverse experimental results into unified theories, and to the investigation of mechanisms of information-processing units, codes, and networks and their relation to capabilities of living systems. This review will emphasize neural models which can be tested by physiological or psychophysical experiments and will include the identification of model parameters based on experimental measurements. An overview of neural firing models, neural interaction models, neural variability and coding, design of input-output experiments for model identification, and description of small network interactions through multiunit and gross potential recording will be included.
神经系统的复杂性给研究者带来了具有挑战性的实验和理论问题。虽然实验研究已使人们对神经元件的生理和解剖学作用有了广泛了解，但仍缺乏对神经系统如何获取和处理信息的全面描述。神经建模致力于对神经生理学实验进行定量解释，将各种实验结果整合到统一理论中，并研究信息处理单元、编码和网络的机制及其与生命系统能力的关系。本综述将重点介绍可通过生理或心理物理学实验进行检验的神经模型，并将包括基于实验测量来识别模型参数。内容将涵盖神经放电模型、神经相互作用模型、神经变异性与编码、用于模型识别的输入 - 输出实验设计，以及通过多单元和总体电位记录对小网络相互作用的描述。
