  Continual learning in computational systems is challenging due to
catastrophic forgetting. We discovered a two layer neural circuit in the fruit
fly olfactory system that addresses this challenge by uniquely combining sparse
coding and associative learning. In the first layer, odors are encoded using
sparse, high dimensional representations, which reduces memory interference by
activating non overlapping populations of neurons for different odors. In the
second layer, only the synapses between odor activated neurons and the output
neuron associated with the odor are modified during learning; the rest of the
weights are frozen to prevent unrelated memories from being overwritten. We
show empirically and analytically that this simple and lightweight algorithm
significantly boosts continual learning performance. The fly associative
learning algorithm is strikingly similar to the classic perceptron learning
algorithm, albeit two modifications, which we show are critical for reducing
catastrophic forgetting. Overall, fruit flies evolved an efficient lifelong
learning algorithm, and circuit mechanisms from neuroscience can be translated
to improve machine computation.
