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 however requires biologically implausible weight transport from feed-forward to feedback paths. We introduce Phaseless Alignment Learning (PAL), 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 forward and backward 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 less neurons and learn more useful latent representations. We demonstrate this on various classification tasks using a cortical microcircuit model with prospective coding.

翻译：皮层感觉处理与学习模型需有效分配所有区域突触的信用。在深度学习中，已知解决方案是误差反向传播，但该方法要求前馈路径向反馈路径传递生物上不合理的权重。我们引入无相位对齐学习（PAL），一种生物合理的方法，用于学习分层皮层层级中的高效反馈权重。该方法通过利用生物物理系统中自然存在的噪声作为额外的信息载体实现。在我们的动态系统中，所有权重同时学习，保持始终开启的可塑性，并仅利用突触局部可用的信息。我们的方法完全无需相位（无前向/反向传递或分阶段学习），能在多层皮层层级间实现高效误差传播，同时维持生物合理的信号传输与学习。该方法适用于广泛模型类别，并在已知生物合理的信用分配方式基础上取得改进：相比随机突触反馈，它能用更少神经元解决复杂任务，并学习更有用的潜在表征。我们通过前瞻编码的皮层微回路模型在各种分类任务中验证了这一点。