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An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features

机译：视觉特征无监督学习的自适应稳态算法

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The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by and large an unsupervised learning process. In the primary visual cortex of mammals, for example, one can observe during development the formation of cells selective to localized, oriented features, which results in the development of a representation in area V1 of images’ edges. This can be modeled using a sparse Hebbian learning algorithms which alternate a coding step to encode the information with a learning step to find the proper encoder. A major difficulty of such algorithms is the joint problem of finding a good representation while knowing immature encoders, and to learn good encoders with a nonoptimal representation. To solve this problem, this work introduces a new regulation process between learning and coding which is motivated by the homeostasis processes observed in biology. Such an optimal homeostasis rule is implemented by including an adaptation mechanism based on nonlinear functions that balance the antagonistic processes that occur at the coding and learning time scales. It is compatible with a neuromimetic architecture and allows for a more efficient emergence of localized filters sensitive to orientation. In addition, this homeostasis rule is simplified by implementing a simple heuristic on the probability of activation of neurons. Compared to the optimal homeostasis rule, numerical simulations show that this heuristic allows to implement a faster unsupervised learning algorithm while retaining much of its effectiveness. These results demonstrate the potential application of such a strategy in machine learning and this is illustrated by showing the effect of homeostasis in the emergence of edge-like filters for a convolutional neural network.
机译：视觉系统中结构的形成，即神经种群内细胞之间的连接，基本上是无监督的学习过程。例如，在哺乳动物的初级视觉皮层中，可以观察到在发育过程中对局部定向特征具有选择性的细胞的形成，这导致图像边缘的区域V1中的表示的发展。这可以使用稀疏的Hebbian学习算法进行建模，该算法将编码步骤替换为对信息进行编码的学习步骤，以找到合适的编码器。这样的算法的主要困难是在知道不成熟的编码器的同时找到良好表示并且学习具有非最佳表示的良好编码器的共同问题。为了解决这个问题，这项工作引入了一种在学习和编码之间的新调节过程，该过程是由生物学中观察到的稳态过程所激发的。通过包括基于非线性函数的自适应机制来实现这种最佳动态平衡，该非线性机制平衡了在编码和学习时间尺度上发生的对立过程。它与仿神经体系结构兼容，可以更有效地出现对方向敏感的局部滤光片。另外，通过对神经元激活概率进行简单的启发式分析，简化了这种稳态规则。与最佳动态平衡规则相比，数值模拟表明，这种启发式算法可以在保持大部分有效性的同时，实现更快的无监督学习算法。这些结果证明了这种策略在机器学习中的潜在应用，并且通过显示动态平衡在卷积神经网络的边缘状滤波器出现中的作用来说明这一点。

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