ArticleFrontiers in neuroscience2020
A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns.
Article in Frontiers in neuroscience, 2020. 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.
- The Difference Neuron-a new spiking neuron model.Biological cybernetics · 2026Article
- Sparse Coding and Temporal Pattern Learning Co-Mediated by Dual Spike-Timing-Dependent Plasticity in a Multilayer Excitatory-Inhibitory Spiking Network.Biomimetics (Basel, Switzerland) · 2026Article
- Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances.Microsystems & nanoengineering · 2025Review
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons.Sensors (Basel, Switzerland) · 2023Article
- Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks.Neural computation · 2023Article
- Heterogeneous recurrent spiking neural network for spatio-temporal classification.Frontiers in neuroscience · 2023Article
- Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.
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