使用数值优化框架测量和理解深度网络中的感官表征
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
摘要：
感觉神经科学的一个核心挑战是描述神经元群体的活动如何代表外部环境的有用特征。然而，虽然神经生理学家早就能够记录清醒的、有行为的动物的神经元反应，但要说一个给定的神经元做什么完全是另一回事。一个关键问题是，在许多感官领域，一个人可能遇到的所有可能刺激的空间实际上是无限的;例如，在视觉中，自然场景是组合复杂的，生物体只会遇到可能刺激的一小部分。因此，即使描述感觉神经元的反应特性也是困难的，并且神经元功能的研究几乎总是受到可以考虑的刺激数量的严格限制。在本文中，我们提出了一个闭环，基于优化的实验框架，用于表征感觉神经元的响应特性，建立在闭环实验方法的过去的努力，并利用人工神经网络的最新进展，作为我们的技术的试验场。具体来说，使用深度卷积神经网络，我们询问现代黑盒优化技术是否可以用于询问深度非线性系统中人工神经元的“调谐景观”，而不会对所考虑的刺激空间施加重大约束。我们引入了一系列措施来量化调整景观，并展示了这些如何与对象识别任务中的网络性能相关。在某种程度上，深度卷积神经网络越来越多地作为生物视觉的事实上的工作假设，我们认为，开发一个统一的方法来研究人工和生物系统具有巨大的潜力，以推动这两个领域一起。
英文摘要：
A central challenge in sensory neuroscience is describing how the activity of populations of neurons can represent useful features of the external environment. However, while neurophysiologists have long been able to record the responses of neurons in awake, behaving animals, it is another matter entirely to say what a given neuron does. A key problem is that in many sensory domains, the space of all possible stimuli that one might encounter is effectively infinite; in vision, for instance, natural scenes are combinatorially complex, and an organism will only encounter a tiny fraction of possible stimuli. As a result, even describing the response properties of sensory neurons is difficult, and investigations of neuronal functions are almost always critically limited by the number of stimuli that can be considered. In this paper, we propose a closed-loop, optimization-based experimental framework for characterizing the response properties of sensory neurons, building on past efforts in closed-loop experimental methods, and leveraging recent advances in artificial neural networks to serve as as a proving ground for our techniques. Specifically, using deep convolutional neural networks, we asked whether modern black-box optimization techniques can be used to interrogate the "tuning landscape" of an artificial neuron in a deep, nonlinear system, without imposing significant constraints on the space of stimuli under consideration. We introduce a series of measures to quantify the tuning landscapes, and show how these relate to the performances of the networks in an object recognition task. To the extent that deep convolutional neural networks increasingly serve as de facto working hypotheses for biological vision, we argue that developing a unified approach for studying both artificial and biological systems holds great potential to advance both fields together.
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