  We present a confidence-based single-layer feed-forward learning algorithm
SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of
activation spikes. We adaptively update a weight vector relying on confidence
estimates and activation offsets relative to previous activity. We regularize
updates proportionally to item-level confidence and weight-specific support,
loosely inspired by the observation from neurophysiology that high spike rates
are sometimes accompanied by low temporal precision. Our experiments suggest
that the new learning algorithm SPIRAL is more robust and less prone to
overfitting than both the averaged perceptron and AROW.
