  Neuromorphic computing with spiking neural networks is promising for
energy-efficient artificial intelligence (AI) applications. However, different
from humans who continually learn different tasks in a lifetime, neural network
models suffer from catastrophic forgetting. How could neuronal operations solve
this problem is an important question for AI and neuroscience. Many previous
studies draw inspiration from observed neuroscience phenomena and propose
episodic replay or synaptic metaplasticity, but they are not guaranteed to
explicitly preserve knowledge for neuron populations. Other works focus on
machine learning methods with more mathematical grounding, e.g., orthogonal
projection on high dimensional spaces, but there is no neural correspondence
for neuromorphic computing. In this work, we develop a new method with neuronal
operations based on lateral connections and Hebbian learning, which can protect
knowledge by projecting activity traces of neurons into an orthogonal subspace
so that synaptic weight update will not interfere with old tasks. We show that
Hebbian and anti-Hebbian learning on recurrent lateral connections can
effectively extract the principal subspace of neural activities and enable
orthogonal projection. This provides new insights into how neural circuits and
Hebbian learning can help continual learning, and also how the concept of
orthogonal projection can be realized in neuronal systems. Our method is also
flexible to utilize arbitrary training methods based on presynaptic
activities/traces. Experiments show that our method consistently solves
forgetting for spiking neural networks with nearly zero forgetting under
various supervised training methods with different error propagation
approaches, and outperforms previous approaches under various settings. Our
method can pave a solid path for building continual neuromorphic computing
systems.
