Activity difference based learning algorithms-such as contrastive Hebbian learning and equilibrium propagation-have been proposed as biologically plausible alternatives to error back-propagation. However, on traditional digital chips these algorithms suffer from having to solve a costly inference problem twice, making these approaches more than two orders of magnitude slower than back-propagation. In the analog realm equilibrium propagation may be promising for fast and energy efficient learning, but states still need to be inferred and stored twice. Inspired by lifted neural networks and compartmental neuron models we propose a simple energy based compartmental neuron model, termed dual propagation, in which each neuron is a dyad with two intrinsic states. At inference time these intrinsic states encode the error/activity duality through their difference and their mean respectively. The advantage of this method is that only a single inference phase is needed and that inference can be solved in layerwise closed-form. Experimentally we show on common computer vision datasets, including Imagenet32x32, that dual propagation performs equivalently to back-propagation both in terms of accuracy and runtime.

翻译：基于活动差异的学习算法——如对比亥布学习和平衡传播——已被提出作为误差反向传播的生物合理替代方案。然而，在传统数字芯片上，这些算法需要求解两次代价高昂的推理问题，导致其速度比反向传播慢两个数量级以上。在模拟领域，平衡传播有望实现快速且能量高效的学习，但仍需两次推断并存储状态。受提升神经网络和区室神经元模型的启发，我们提出了一种简单的基于能量的区室神经元模型，称为双传播，其中每个神经元是一个具有两种内在状态的二元体。在推理时，这些内在状态通过其差异和均值分别编码误差/活动二元性。该方法优势在于仅需一次推理阶段，且推理可通过逐层闭式求解。实验表明，在包括ImageNet32x32在内的常见计算机视觉数据集上，双传播在准确性和运行时方面均与反向传播性能相当。