Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex
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DOI：
10.1162/neco_a_00268
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发表时间：
2012-05-01
影响因子：
2.9
通讯作者：
Hamker, Fred
中科院分区：
文献类型：
--
作者：
Teichmann, Michael;Wiltschut, Jan;Hamker, Fred
人类视觉系统具有显著的能力，可以识别位置、旋转和比例不变的物体。对神经生物学发现的一个很好的解释涉及到模拟视觉皮层信号处理的计算模型。在某种程度上，这可能是从视觉感知的早期到晚期区域逐步实现的。虽然已经提出了几种算法来学习特征检测器，但只有少数研究涉及这种不变性的生物学合理学习问题。在这项研究中，提出了一套赫布学习规则的基础上钙动力学和稳态调节的单神经元。它们的性能在初级视觉皮层的简单模型中得到验证，以基于一系列静态图像来学习所谓的复杂细胞。因此，学习到的复杂细胞反应在很大程度上对相位和位置是不变的。
The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.