Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, which requires biologically implausible weight transport from feed-forwards to feedback paths. We introduce phaseless alignment learning, a bio-plausible method to learn efficient feedback weights in layered cortical hierarchies. This is achieved by exploiting the noise naturally found in biophysical systems as an additional carrier of information. In our dynamical system, all weights are learned simultaneously with always-on plasticity and using only information locally available to the synapses. Our method is completely phase-free (no forwards and backwards passes or phased learning) and allows for efficient error propagation across multi-layer cortical hierarchies, while maintaining biologically plausible signal transport and learning. Our method is applicable to a wide class of models and improves on previously known biologically plausible ways of credit assignment: compared to random synaptic feedback, it can solve complex tasks with fewer neurons and learn more useful latent representations. We demonstrate this on various classification tasks using a cortical microcircuit model with prospective coding.

皮层的感觉处理和学习模型需要有效地将功劳分配给所有区域的突触。在深度学习中，一个已知的解决方案是误差反向传播，它需要从前馈路径到反馈路径的生物学上难以置信的权重传输。我们引入了无相对齐学习，这是一种在分层皮质层次结构中学习有效反馈权重的生物合理方法。这是通过利用生物物理系统中自然存在的噪声作为附加信息载体来实现的。在我们的动态系统中，所有权重都是通过始终可用的可塑性同时学习的，并且仅使用突触本地可用的信息。我们的方法是完全无阶段的（没有前向和后向传递或分阶段学习），并且允许跨多层皮质层次结构有效地传播错误，同时保持生物学上合理的信号传输和学习。我们的方法适用于多种模型，并改进了先前已知的生物学上合理的信用分配方式：与随机突触反馈相比，它可以用更少的神经元解决复杂的任务并学习更有用的潜在表示。我们使用具有前瞻性编码的皮质微电路模型在各种分类任务中证明了这一点。