Visual Explanations from Spiking Neural Networks using Interspike Intervals
This provides a visualization tool for SNNs, addressing a gap in interpretability for researchers in neuromorphic computing, though it is incremental as it builds on existing SNN training methods.
The authors tackled the lack of visualization tools for Spiking Neural Networks (SNNs) by proposing Spike Activation Map (SAM), a bio-plausible method that generates attention maps without gradients, highlighting discriminative image regions and enabling analysis of internal spike behavior across training configurations.
Spiking Neural Networks (SNNs) compute and communicate with asynchronous binary temporal events that can lead to significant energy savings with neuromorphic hardware. Recent algorithmic efforts on training SNNs have shown competitive performance on a variety of classification tasks. However, a visualization tool for analysing and explaining the internal spike behavior of such temporal deep SNNs has not been explored. In this paper, we propose a new concept of bio-plausible visualization for SNNs, called Spike Activation Map (SAM). The proposed SAM circumvents the non-differentiable characteristic of spiking neurons by eliminating the need for calculating gradients to obtain visual explanations. Instead, SAM calculates a temporal visualization map by forward propagating input spikes over different time-steps. SAM yields an attention map corresponding to each time-step of input data by highlighting neurons with short inter-spike interval activity. Interestingly, without both the backpropagation process and the class label, SAM highlights the discriminative region of the image while capturing fine-grained details. With SAM, for the first time, we provide a comprehensive analysis on how internal spikes work in various SNN training configurations depending on optimization types, leak behavior, as well as when faced with adversarial examples.