非线性赫布学习作为感受野形成的统一原则
Nonlinear Hebbian learning as a unifying principle in receptive field formation
摘要：
感觉感受野的发育在过去已经通过各种模型来建模，包括诸如稀疏编码或独立成分分析的规范模型和诸如尖峰定时依赖可塑性或突触可塑性的Bienenstock-Cooper-Munro模型的自底向上模型。在这里，我们表明，上述各种方法都可以统一到一个共同的原则，即非线性赫布学习。当非线性赫布学习应用于自然图像时，感受野形状受到输入统计和预处理的强烈约束，但在神经元模型或突触可塑性规则中的不同非线性选择之间仅表现出适度的变化。局部感受野的形成既不需要过度完整性，也不需要稀疏的网络活动。对听觉模型或V2发展等替代感觉方式的分析得出了相同的结论。在所有的例子中，感受野可以通过将抽象模型重新表述为非线性赫布学习来先验地预测。因此，非线性赫布学习和自然统计可以解释跨模型和感觉模态的感受野形成的许多方面。
英文摘要：
The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.
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