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.

中文翻译：

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通过系统识别完成感觉神经元的功能表征。

系统识别是一种越来越多的感觉神经生理学方法，它有助于发展感觉过程的定量功能模型。此方法提供了一组清晰的指南，用于将实验数据与其他有关感觉功能的知识相结合，以获得能够最佳预测神经元处理感觉信息的方式的描述。该预测范例为评估和比较计算模型提供了一种客观的方法。在本章中，我们回顾了已在感觉神经生理学中使用的许多系统识别算法，并展示了如何将它们视为单个统计推断问题的变体。然后，我们回顾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已应用到的几个问题，包括感觉神经科学的一个重要的长期目标：开发能在完全自然条件下准确预测神经元反应的感觉系统模型。