  Events in the real world are correlated across nearby points in time, and we
must learn from this temporally smooth data. However, when neural networks are
trained to categorize or reconstruct single items, the common practice is to
randomize the order of training items. What are the effects of temporally
smooth training data on the efficiency of learning? We first tested the effects
of smoothness in training data on incremental learning in feedforward nets and
found that smoother data slowed learning. Moreover, sampling so as to minimize
temporal smoothness produced more efficient learning than sampling randomly. If
smoothness generally impairs incremental learning, then how can networks be
modified to benefit from smoothness in the training data? We hypothesized that
two simple brain-inspired mechanisms, leaky memory in activation units and
memory-gating, could enable networks to rapidly extract useful representations
from smooth data. Across all levels of data smoothness, these brain-inspired
architectures achieved more efficient category learning than feedforward
networks. This advantage persisted, even when leaky memory networks with gating
were trained on smooth data and tested on randomly-ordered data. Finally, we
investigated how these brain-inspired mechanisms altered the internal
representations learned by the networks. We found that networks with
multi-scale leaky memory and memory-gating could learn internal representations
that un-mixed data sources which vary on fast and slow timescales across
training samples. Altogether, we identified simple mechanisms enabling neural
networks to learn more quickly from temporally smooth data, and to generate
internal representations that separate timescales in the training signal.
