ArticleFrontiers in neuroscience2021
Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Temporal coding enables hyperacuity in event-based vision.Nature communications · 2026Article
- Event-Based Vision at the Edge: A Review.Brain sciences · 2026Review
- Memristor Synapse-A Device-Level Critical Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Neuromorphic computing at scale.Nature · 2025Review
- Neuromorphic engineering: Artificial brains for artificial intelligence.Annals of the New York Academy of Sciences · 2024Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science advances · 2023Article
- First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures.Frontiers in neuroscience · 2023Article
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
- Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition.Frontiers in neuroscience · 2023Article
- Event-Based Trajectory Prediction Using Spiking Neural Networks.Frontiers in computational neuroscience · 2021Article
- Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).Frontiers in neuroscience · 2020Article
- Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection.Frontiers in neurorobotics · 2019Article
- An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
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Authors and funding
3 authors.
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Abstract
A major characteristic of spiking neural networks (SNNs) over conventional artificial neural networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and efficient computing. In this paper, we assess if neuromorphic datasets recorded from static images are able to evaluate the ability of SNNs to use spike timings in their calculations. We have analyzed N-MNIST, N-Caltech101 and DvsGesture along these lines, but focus our study on N-MNIST. First we evaluate if additional information is encoded in the time domain in a neuromorphic dataset. We show that an ANN trained with backpropagation on frame-based versions of N-MNIST and N-Caltech101 images achieve 99.23 and 78.01% accuracy. These are comparable to the state of the art-showing that an algorithm that purely works on spatial data can classify these datasets. Second we compare N-MNIST and DvsGesture on two STDP algorithms, RD-STDP, that can classify only spatial data, and STDP-tempotron that classifies spatiotemporal data. We demonstrate that RD-STDP performs very well on N-MNIST, while STDP-tempotron performs better on DvsGesture. Since DvsGesture has a temporal dimension, it requires STDP-tempotron, while N-MNIST can be adequately classified by an algorithm that works on spatial data alone. This shows that precise spike timings are not important in N-MNIST. N-MNIST does not, therefore, highlight the ability of SNNs to classify temporal data. The conclusions of this paper open the question-what dataset can evaluate SNN ability to classify temporal data?
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