Department of Physics, University of California, Berkeley, Berkeley, California, United States of America.
PLoS Comput Biol. 2013;9(8):e1003182. doi: 10.1371/journal.pcbi.1003182. Epub 2013 Aug 29.
The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.
稀疏编码假说在基于自然场景的统计数据预测初级视觉皮层 (V1) 中简单细胞的反应特性方面取得了很大的成功。在典型的稀疏编码模型中，模型神经元的活动和感受野被优化为使用最少的神经活动来准确表示输入刺激。随着这些网络的发展来表示给定类别的刺激，感受野得到了改进，从而捕获最重要的刺激特征。直观地说，这预计会导致网络活动随时间变得更加稀疏。然而，最近的实验表明，雪貂 V1 中由刺激引起的活动在发育过程中变得不那么稀疏，这对稀疏编码假说提出了明显的挑战。在这里，我们证明了一些稀疏编码模型，例如那些对神经元发射率使用同型机制的模型，在学习过程中可以表现出稀疏性的降低，同时仍然与成熟 V1 感受野形状和相当稀疏的成熟网络状态很好地吻合。我们的结论是，观察到的发展趋势本身并不排除稀疏性作为神经编码的原则：即使在发育过程中稀疏性略有降低，成熟的网络也可以进行稀疏编码。为了在模型和生理感受野之间进行比较，我们引入了一种新的非参数方法，使用图像配准技术比较感受野形状。
