Wu Michael C-K, David Stephen V, Gallant Jack L
Biophysics Graduate Group, University of California, Berkeley, California 94720, USA.
Annu Rev Neurosci. 2006;29:477-505. doi: 10.1146/annurev.neuro.29.051605.113024.
System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.
系统识别是感觉神经生理学中一种不断发展的方法，它有助于建立感觉处理的定量功能模型。这种方法为将实验数据与关于感觉功能的其他知识相结合提供了一套清晰的指导方针，以获得一种能最佳预测神经元处理感觉信息方式的描述。这种预测范式为评估和比较计算模型提供了一种客观方法。在本章中，我们回顾了许多在感觉神经生理学中使用的系统识别算法，并展示了它们如何被视为单个统计推断问题的变体。然后，我们回顾了将这些方法应用于神经生理学实验时出现的许多实际问题：刺激选择、行为控制、模型可视化和验证。最后，我们讨论了系统识别最近应用的几个问题，包括感觉神经科学的一个重要长期目标：开发在完全自然条件下能准确预测神经元反应的感觉系统模型。
