Existing machines are functionally specific tools that were made for easy prediction and control. Tomorrow's machines may be closer to biological systems in their mutability, resilience, and autonomy. But first they must be capable of learning and retaining new information without being exposed to it arbitrarily often. Past efforts to engineer such systems have sought to build or regulate artificial neural networks using disjoint sets of weights that are uniquely sensitive to specific tasks or inputs. This has not yet enabled continual learning over long sequences of previously unseen data without corrupting existing knowledge: a problem known as catastrophic forgetting. In this paper, we introduce a system that can learn sequentially over previously unseen datasets (ImageNet, CIFAR-100) with little forgetting over time. This is done by controlling the activity of weights in a convolutional neural network on the basis of inputs using top-down regulation generated by a second feed-forward neural network. We find that our method learns continually under domain transfer with sparse bursts of activity in weights that are recycled across tasks, rather than by maintaining task-specific modules. Sparse synaptic bursting is found to balance activity and suppression such that new functions can be learned without corrupting extant knowledge, thus mirroring the balance of order and disorder in systems at the edge of chaos. This behavior emerges during a prior pre-training (or 'meta-learning') phase in which regulated synapses are selectively disinhibited, or grown, from an initial state of uniform suppression through prediction error minimization.

翻译：现有机器是为易于预测和控制而制造的功能特定工具。未来的机器可能在可塑性、鲁棒性和自主性上更接近生物系统，但首先它们必须具备在不被任意频繁暴露于新信息的情况下学习并保留新知识的能力。过去构建此类系统的尝试，致力于通过构建或调控具有独特任务/输入敏感性的不交权重集的人工神经网络。这尚未能实现在不破坏已有知识的前提下，对长序列未知数据进行持续学习——这一难题被称为灾难性遗忘。本文提出一种系统，可在ImageNet、CIFAR-100等未见数据集上顺序学习，且随时间推移遗忘极少。该系统通过第二个前馈神经网络生成的自顶向下调控，基于输入控制卷积神经网络中权重的活动。我们发现，该方法在域迁移下通过跨任务复用的权重的稀疏爆发活动实现持续学习，而非维护任务特异性模块。稀疏突触爆发能够平衡激活与抑制，使得在不破坏既有知识的前提下习得新功能，从而镜像了混沌边缘系统中有序与无序的平衡。这种行为产生于先验预训练（即"元学习"）阶段——在该阶段中，受调控的突触通过预测误差最小化，从初始均匀抑制状态被选择性去抑制（即生长）。