ArticlePatterns (New York, N.Y.)2022
Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Neuro-Geometric Graph Transformers with Differentiable Radiographic Geometry for Spinal X-Ray Image Analysis.Journal of imaging · 2026Article
- Design of CMOS-memristor hybrid synapse and its application for noise-tolerant memristive spiking neural network.Frontiers in neuroscience · 2025Article
- Emergence of brain-inspired small-world spiking neural network through neuroevolution.iScience · 2024Article
- SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications.Frontiers in neuroscience · 2024Article
- Brain-inspired neural circuit evolution for spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
- Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks.Brain sciences · 2023Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
- Models developed for spiking neural networks.MethodsX · 2023Article
- Heterogeneous recurrent spiking neural network for spatio-temporal classification.Frontiers in neuroscience · 2023Article
- N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.Scientific data · 2022Article
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Authors and funding
3 authors.
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Abstract
The spiking neural network (SNN) mimics the information-processing operation in the human brain. Directly applying backpropagation to the training of the SNN still has a performance gap compared with traditional deep neural networks. To address the problem, we propose a biologically plausible spatial adjustment that rethinks the relationship between membrane potential and spikes and realizes a reasonable adjustment of gradients to different time steps. It precisely controls the backpropagation of the error along the spatial dimension. Secondly, we propose a biologically plausible temporal adjustment to make the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of traditional spiking neurons. We have verified our algorithm on several datasets, and the experimental results have shown that our algorithm greatly reduces network latency and energy consumption while also improving network performance.
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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.