Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Chenxi Sun Hongyan Li Moxian Song Derun Can Shenda Hong
Spiking Neural Networks (SNNs) have a greater potential for modeling time
series data than Artificial Neural Networks (ANNs), due to their inherent
neuron dynamics and low energy consumption. However, it is difficult to
demonstrate their superiority in classification accuracy, because current
efforts mainly focus on designing better network structures. In this work,
enlighten by brain-inspired science, we find that, not only the structure but
also the learning process should be human-like. To achieve this, we investigate
the power of Curriculum Learning (CL) on SNNs by designing a novel method named
CSNN with two theoretically guaranteed mechanisms: The active-to-dormant
training order makes the curriculum similar to that of human learning and
suitable for spiking neurons; The value-based regional encoding makes the
neuron activity to mimic the brain memory when learning sequential data.
Experiments on multiple time series sources including simulated, sensor,
motion, and healthcare demonstrate that CL has a more positive effect on SNNs
than ANNs with about twice the accuracy change, and CSNN can increase about 3%
SNNs' accuracy by improving network sparsity, neuron firing status, anti-noise
ability, and convergence speed.