ArticleScientific reports2021
Visual explanations from spiking neural networks using inter-spike intervals.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Unsupervised post-training learning in spiking neural networks.Scientific reports · 2025Article
- Biologically Inspired Spatial-Temporal Perceiving Strategies for Spiking Neural Network.Biomimetics (Basel, Switzerland) · 2025Article
- Thermal effects and ephaptic entrainment in Hodgkin-Huxley model.Scientific reports · 2024Article
- Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding.Materials (Basel, Switzerland) · 2024Article
- A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm.Scientific reports · 2023Article
- Gradient-based feature-attribution explainability methods for spiking neural networks.Frontiers in neuroscience · 2023Article
- Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.Patterns (New York, N.Y.) · 2022Article
- Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning.Frontiers in neuroscience · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By emulating biological features in brain, Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional deep learning. To make SNNs ubiquitous, a 'visual explanation' technique for analysing and explaining the internal spike behavior of such temporal deep SNNs is crucial. Explaining SNNs visually will make the network more transparent giving the end-user a tool to understand how SNNs make temporal predictions and why they make a certain decision. In this paper, we propose a bio-plausible visual explanation tool for SNNs, called Spike Activation Map (SAM). SAM yields a heatmap (i.e., localization map) corresponding to each time-step of input data by highlighting neurons with short inter-spike interval activity. Interestingly, without the use of gradients and ground truth, SAM produces a temporal localization map highlighting the region of interest in an image attributed to an SNN's prediction at each time-step. Overall, SAM outsets the beginning of a new research area 'explainable neuromorphic computing' that will ultimately allow end-users to establish appropriate trust in predictions from SNNs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.