Title: Towards Biologically Plausible Convolutional Networks Show Chinese title

Title: 走向生物合理的卷积网络

Abstract: Convolutional networks are ubiquitous in deep learning. They are particularly useful for images, as they reduce the number of parameters, reduce training time, and increase accuracy. However, as a model of the brain they are seriously problematic, since they require weight sharing - something real neurons simply cannot do. Consequently, while neurons in the brain can be locally connected (one of the features of convolutional networks), they cannot be convolutional. Locally connected but non-convolutional networks, however, significantly underperform convolutional ones. This is troublesome for studies that use convolutional networks to explain activity in the visual system. Here we study plausible alternatives to weight sharing that aim at the same regularization principle, which is to make each neuron within a pool react similarly to identical inputs. The most natural way to do that is by showing the network multiple translations of the same image, akin to saccades in animal vision. However, this approach requires many translations, and doesn't remove the performance gap. We propose instead to add lateral connectivity to a locally connected network, and allow learning via Hebbian plasticity. This requires the network to pause occasionally for a sleep-like phase of "weight sharing". This method enables locally connected networks to achieve nearly convolutional performance on ImageNet and improves their fit to the ventral stream data, thus supporting convolutional networks as a model of the visual stream. Show Chinese abstract

Abstract: 卷积网络在深度学习中无处不在。 它们对于图像特别有用，因为它们减少了参数数量，减少了训练时间，并提高了准确性。 然而，作为大脑的模型，它们存在严重问题，因为它们需要权重共享——真实神经元根本无法做到这一点。 因此，尽管大脑中的神经元可以局部连接（卷积网络的一个特征），但它们不能是卷积的。 然而，局部连接但非卷积的网络显著表现不如卷积网络。 这对于使用卷积网络来解释视觉系统活动的研究来说是一个麻烦。 在这里，我们研究了替代权重共享的合理方法，这些方法旨在实现相同的正则化原则，即让池中的每个神经元对相同输入做出相似的反应。 最自然的方法是向网络展示同一图像的多次平移，类似于动物视觉中的扫视。 然而，这种方法需要许多平移，并不能消除性能差距。 我们建议相反的是，在局部连接网络中添加侧向连接，并通过赫布可塑性进行学习。 这要求网络偶尔暂停，进入类似睡眠的“权重共享”阶段。 这种方法使局部连接网络能够在ImageNet上达到几乎卷积的性能，并改善它们与腹侧流数据的拟合度，从而支持卷积网络作为视觉流的模型。