  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.
