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.