Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of the current task dynamically. The algorithm alternates between updating the model weights and a latent task embedding, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. On a continual learning benchmark, it achieves competitive end average accuracy by mitigating forgetting, but importantly, by requiring the model to adapt through latent updates, it organizes knowledge into flexible structures with a cognitive interface to control them. Tasks later in the sequence can be solved through knowledge transfer as they become reachable within the well-factorized latent space. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain.

翻译：动物在不断变化的环境中茁壮成长，并利用时间结构学习良好分解的因果表征。相比之下，传统神经网络在变化环境中容易遭受遗忘，目前已有众多方法通过不同权衡来限制遗忘。受大脑丘脑皮层回路的启发，我们提出一种简单算法，利用推理时刻的优化动态生成当前任务的内部表征。该算法交替更新模型权重与潜在任务嵌入，使智能体能够将时间经验流解析为离散事件，并组织关于这些事件的学习。在持续学习基准测试中，它通过缓解遗忘获得具有竞争力的平均末端准确率，但更重要的是，通过要求模型通过潜在更新进行适应，它将知识组织成灵活结构，并配备认知接口进行控制。序列中后续任务可通过知识迁移解决，因为这些任务变得可在良好分解的潜在空间内达到。该算法满足开放环境中理想持续学习智能体的许多期望特性，其简洁性暗示着在具有丰富反馈控制环路（如大脑丘脑皮层回路）的电路中存在基础计算机制。