The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning rely on stochastic gradient descent and its variants that employ global error updates, and hence need to adopt strategies such as memory buffers or replay to circumvent its stability, greed, and short-term memory limitations. To address this limitation, we have developed a biologically inspired lightweight neural network architecture that incorporates synaptic plasticity mechanisms and neuromodulation and hence learns through local error signals to enable online continual learning without stochastic gradient descent. Our approach leads to superior online continual learning performance on Split-MNIST, Split-CIFAR-10, and Split-CIFAR-100 datasets compared to other memory-constrained learning approaches and matches that of the state-of-the-art memory-intensive replay-based approaches. We further demonstrate the effectiveness of our approach by integrating key design concepts into other backpropagation-based continual learning algorithms, significantly improving their accuracy. Our results provide compelling evidence for the importance of incorporating biological principles into machine learning models and offer insights into how we can leverage them to design more efficient and robust systems for online continual learning.

翻译：在不发生灾难性遗忘的情况下，从持续输入的数据流中学习是设计智能系统的关键。许多持续学习方法依赖于随机梯度下降及其采用全局误差更新的变体，因此需要采用记忆缓冲区或重放等策略来规避其稳定性、贪婪性和短期记忆局限性。为解决这一局限性，我们开发了一种受生物启发的轻量级神经网络架构，该架构整合了突触可塑性机制和神经调节，从而通过局部误差信号进行学习，无需随机梯度下降即可实现在线持续学习。与其他受内存限制的学习方法相比，我们的方法在Split-MNIST、Split-CIFAR-10和Split-CIFAR-10数据集上实现了更优的在线持续学习性能，并可媲美最先进的高内存重放方法。我们进一步将关键设计概念整合到其他基于反向传播的持续学习算法中，显著提升其准确性，从而证明了该方法的有效性。我们的结果为将生物原理融入机器学习模型的重要性提供了有力证据，并揭示了如何利用这些原理设计更高效、更鲁棒的在线持续学习系统。