ArticleFrontiers in neuroscience2022
Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Sensitivity of Spiking Neural Networks Due to Input Perturbation.Brain sciences · 2024Article
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- Learning cortical hierarchies with temporal Hebbian updates.Frontiers in computational neuroscience · 2023Article
- A directionally selective collision-sensing visual neural network based on fractional-order differential operator.Frontiers in neurorobotics · 2023Article
- Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications.Frontiers in computational neuroscience · 2023Review
- Ring attractor bio-inspired neural network for social robot navigation.Frontiers in neurorobotics · 2023Article
- Gradient-based feature-attribution explainability methods for spiking neural networks.Frontiers in neuroscience · 2023Article
- Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.Frontiers in computational neuroscience · 2023Article
- A systematic comparison of deep learning methods for EEG time series analysis.Frontiers in neuroinformatics · 2023Article
- UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.Sensors (Basel, Switzerland) · 2022Article
- Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine.Frontiers in neuroinformatics · 2022Article
- Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.Frontiers in computational neuroscience · 2022Article
- Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic systemFrontiers in computational neuroscience · 2022Article
- Extended liquid state machines for speech recognition.Frontiers in neuroscience · 2022Article
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3 authors.
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Abstract
Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the few-shot learning performance of artificial neural networks. Importantly, existing spike-based few-shot learning models do not target robust learning based on spatiotemporal dynamics and superior machine learning theory. In this paper, we propose a novel spike-based framework with the entropy theory, namely, heterogeneous ensemble-based spike-driven few-shot online learning (HESFOL). The proposed HESFOL model uses the entropy theory to establish the gradient-based few-shot learning scheme in a recurrent SNN architecture. We examine the performance of the HESFOL model based on the few-shot classification tasks using spiking patterns and the Omniglot data set, as well as the few-shot motor control task using an end-effector. Experimental results show that the proposed HESFOL scheme can effectively improve the accuracy and robustness of spike-driven few-shot learning performance. More importantly, the proposed HESFOL model emphasizes the application of modern entropy-based machine learning methods in state-of-the-art spike-driven learning algorithms. Therefore, our study provides new perspectives for further integration of advanced entropy theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems.
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