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.

使用生物学原理进入机器学习模型，我们开发了一种轻量级神经网络结构，通过突触可塑性机制和神经调制，通过本地错误信号进行在线继续学习，从而改善了传统方法的稳定性和记忆限制，在 Split-MNIST，Split-CIFAR-10 和 Split-CIFAR-100 数据集上表现出更好的在线继续学习性能，并在基于回放的方法上达到最先进的记忆密集型方法的性能水平，进一步通过将关键设计概念整合到其他反向传播的继续学习算法中，显著提高了它们的准确性。