mlsanityData to 7 Oct 2026

Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation

arXiv 2302.07328 ↗First posted 14 Feb 2023cs.NE cs.AI cs.CVcited by 5

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Abstract (arXiv)

Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data. However, ANNs typically require significant power and memory consumptions to reach their full potential. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity nature. SNN, however, are not as easy to train as ANNs. In this work, we propose a hybrid SNN training scheme and apply it to segment human hippocampi from magnetic resonance images. Our approach takes ANN-SNN conversion as an initialization step and relies on spike-based backpropagation to fine-tune the network. Compared with the conversion and direct training solutions, our method has advantages in both segmentation accuracy and training efficiency. Experiments demonstrate the effectiveness of our model in achieving the design goals.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

16 of 27 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  3. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  4. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  5. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  6. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  7. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  8. 2015Spiking Deep Networks with LIF Neuronscited by 63
  9. 2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 54
  10. 2016Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networkscited by 31
  11. 2021A Spiking Neural Network for Image Segmentationcited by 27
  12. 2020TCL: an ANN-to-SNN Conversion with Trainable Clipping Layerscited by 20
4 more
  1. 2019Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neuronscited by 14
  2. 2018NengoDL: Combining deep learning and neuromorphic modelling methodscited by 11
  3. 2021Keys to Accurate Feature Extraction Using Residual Spiking Neural Networkscited by 10
  4. 2022Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenariocited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2023Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropoutcited by 5
  2. 2025Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approachcited by 5
  3. 2023Joint ANN-SNN Co-training for Object Localization and Image Segmentationcited by 1
  4. 2023Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memorycited by 0
  5. 2026Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Trainingcited by 0