ArticleEntropy (Basel, Switzerland)2022
Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- AI-driven framework for accurate detection of Alzheimer's disease in EEG.Scientific reports · 2026Article
- Spiking neural networks for nonlinear regression.Royal Society open science · 2024Article
- Article
- SLoN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels.Frontiers in neuroscience · 2024Article
- Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.Frontiers in neurorobotics · 2024Article
- Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.Frontiers in computational neuroscience · 2024Article
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Image Watermarking Using Least Significant Bit and Canny Edge Detection.Sensors (Basel, Switzerland) · 2023Article
- Learning cortical hierarchies with temporal Hebbian updates.Frontiers in computational neuroscience · 2023Article
- A systematic comparison of deep learning methods for EEG time series analysis.Frontiers in neuroinformatics · 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
- ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator.Frontiers in neuroscience · 2023Article
- UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.Sensors (Basel, Switzerland) · 2022Article
- Combining Optical Coherence Tomography and Fundus Photography to Improve Glaucoma Screening.Diagnostics (Basel, Switzerland) · 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
- Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine.Frontiers in neuroinformatics · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by 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 online meta-learning performance of artificial neural networks. Importantly, existing spike-based online meta-learning models do not target the robust learning based on spatio-temporal dynamics and superior machine learning theory. In this invited article, we propose a novel spike-based framework with minimum error entropy, called MeMEE, using the entropy theory to establish the gradient-based online meta-learning scheme in a recurrent SNN architecture. We examine the performance based on various types of tasks, including autonomous navigation and the working memory test. The experimental results show that the proposed MeMEE model can effectively improve the accuracy and the robustness of the spike-based meta-learning performance. More importantly, the proposed MeMEE model emphasizes the application of the modern information theoretic learning approach on the state-of-the-art spike-based learning algorithms. Therefore, in this invited paper, we provide new perspectives for further integration of advanced information 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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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.