Spike-timing-dependent plasticity can be rephrased as noisy gradient, which allows to obtain convergence guarantees for the stochastic learning dynamics.

Our work effectively bridges Hebbian principles with explicit representation learning objectives, demonstrating considerable potential and biological plausibility.

Using theory and simulations, we show that task-irrelevant stimuli could cause long-term drift of neural representations