Unsupervised Sparse Expansion for Continual Learning in Fly-Inspired Spiking Networks

Artificial neural networks trained on a sequence of tasks tend to overwrite the knowledge of earlier tasks, whereas animals continue to acquire memories throughout life. The fruit-fly mushroom body motivates sparse expansion as one way to reduce this interference, but computational models usually freeze randomly sampled projection-neuron–Kenyon-cell connections. We study whether shaping these connections from unlabeled input improves later class-incremental learning in a spiking network. A global orthogonalizing update is nattractive at Kenyon-cell populations of thousands, where the residual would have to be accumulated over the whole population for every unit and the rule itself is non-local, so we apply it within small groups whose composition is redrawn during training; in expectation the pairwise interaction then becomes symmetric — the form of anti-Hebbian decorrelation — instead of the sequential recursion of the original rule. The learned connections are given the same number of inputs per cell and the same weight norm as the random baseline, frozen, and only a shared readout is trained. Across a synthetic odor stream and two image benchmarks, the learned mapping improves final class-incremental accuracy without labeled replay or test-time task identity. Feature measurements show lower class and task correlation on the image inputs, although active-set overlap increases slightly on the odor stream. The method combines an unsupervised feature stage with a biologically inspired spiking circuit, while requiring access to unlabeled inputs from future classes.