Adaptive stimulus optimization for sensory systems neuroscience.
Adaptive stimulus optimization for sensory systems neuroscience.
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DOI：
10.3389/fncir.2013.00101
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发表时间：
2013
影响因子：
3.5
通讯作者：
Zhang K
中科院分区：
文献类型：
--
作者：
DiMattina C;Zhang K
在本文中，我们回顾了最近几条旨在开发用于感觉神经生理学的自适应在线刺激生成实用方法的工作。我们考虑了利用在线刺激优化的各种实验范式，包括经典的最优刺激范式，其实验目标是识别最大化神经反应的刺激，等反应范式，其发现产生恒定反应的刺激集，以及系统识别范式，其实验目标是估计并可能比较感觉处理模型。我们讨论了自适应射击率优化的各种理论和实践方面，包括具有刺激空间约束的优化，射击率自适应以及最优刺激的可能网络约束。我们考虑了系统辨识问题，并展示了非线性模型的准确估计如何高度依赖于用于探测网络的刺激集。我们认为，优化刺激以准确的模型估计可能使成功识别非线性模型成为可能，否则难以处理，并总结了最近这类研究的几个。最后，我们提出了一个两阶段的刺激设计过程，它结合了模型估计和模型比较的双重目标，对于事先未知适当模型的系统识别实验可能特别有用。我们提出，通过提高计算机能力实现快速在线刺激优化，可以使感觉神经科学从描述性范式转向实时模型估计和比较的新范式。
In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison.
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