Spikemoid: Updated Spike-based Loss Methods for Classification

Spiking Neural Networks (SNNs) have gained research attention in recent years due to their potential as low-power computing architectures for deployment on neuromorphic hardware. The introduction of offline training capabilities like Spike Layer Error Reassignment in Time (SLAYER) and advancements in the probabilistic interpretations of SNN output reinforce SNNs as a viable alternative to Artificial Neural Networks (ANNs). Spikemax was previously introduced as a family of differentiable loss methods which use windowed spike counts to form classification probabilities. We modify the spikemaxS loss method to use rates and a scaling parameter instead of counts to form scaled-spikemax. Our mathematical analysis shows that an appropriate scaling term can yield less coarse probability outputs from the SNN and help smooth the gradient of the loss during training. Experimentally, we show that scaled-spikemax achieves faster training convergence than spikemax and results in relative improvements of 4.2% and 9.9% in accuracy for NMNIST and N-TIDIGITS18, respectively We then extend scaled-spikemax to construct a spike-based loss function for multi-label classification called spikemoid. The viability of spikemoid is shown via the first known multi-label classification results on N-TIDIGITS18 and 2NMNIST, a novel variation of NMNIST that superimposes event-driven sensory data.