Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning

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.

[1]  Jiuwen Cao,et al.  Robust Maximum Mixture Correntropy Criterion Based One-Class Classification Algorithm , 2022, IEEE Intelligent Systems.

[2]  Priyadarshini Panda,et al.  Beyond classification: directly training spiking neural networks for semantic segmentation , 2021, Neuromorph. Comput. Eng..

[3]  Jiang Wang,et al.  Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Badong Chen,et al.  Mixture Correntropy-Based Kernel Extreme Learning Machines , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Huajin Tang,et al.  Few-Shot Learning in Spiking Neural Networks by Multi-Timescale Optimization , 2021, Neural Computation.

[6]  Leandros Tassiulas,et al.  Federated Learning With Spiking Neural Networks , 2021, IEEE Transactions on Signal Processing.

[7]  Badong Chen,et al.  Effects of Outliers on the Maximum Correntropy Estimation: A Robustness Analysis , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  Tiejun Huang,et al.  Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks , 2021, IJCAI.

[9]  Priyadarshini Panda,et al.  PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks , 2021, ArXiv.

[10]  Priyadarshini Panda,et al.  Visual explanations from spiking neural networks using inter-spike intervals , 2021, Scientific Reports.

[11]  Jiang Wang,et al.  Efficient Spike-Driven Learning With Dendritic Event-Based Processing , 2021, Frontiers in Neuroscience.

[12]  Abdulmotaleb El Saddik,et al.  Sitting Posture Recognition Using a Spiking Neural Network , 2021, IEEE Sensors Journal.

[13]  Nick Barnes,et al.  Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yujie Wu,et al.  Going Deeper With Directly-Trained Larger Spiking Neural Networks , 2020, AAAI.

[15]  P. Panda,et al.  Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch , 2020, Frontiers in Neuroscience.

[16]  Lionel M. Ni,et al.  Generalizing from a Few Examples , 2020, ACM Comput. Surv..

[17]  Matthias Schmid,et al.  Bias in Cross-Entropy-Based Training of Deep Survival Networks , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Badong Chen,et al.  Correntropy-Based Multiview Subspace Clustering , 2019, IEEE Transactions on Cybernetics.

[19]  Yuri Tolkach,et al.  High-accuracy prostate cancer pathology using deep learning , 2020, Nature Machine Intelligence.

[20]  Kirk Y. W. Scheper,et al.  Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Yang Liu,et al.  Federated Learning , 2019, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[22]  Kaushik Roy,et al.  Towards spike-based machine intelligence with neuromorphic computing , 2019, Nature.

[23]  Mingguo Zhao,et al.  Towards artificial general intelligence with hybrid Tianjic chip architecture , 2019, Nature.

[24]  Nicholas Soures,et al.  Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition , 2019, Front. Neurosci..

[25]  Patrick Pérez,et al.  Boosting Few-Shot Visual Learning With Self-Supervision , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[26]  Parami Wijesinghe,et al.  Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines , 2019, Front. Neurosci..

[27]  Yingsong Li,et al.  Maximum Correntropy Criterion With Variable Center , 2019, IEEE Signal Processing Letters.

[28]  Pierre Tirilly,et al.  Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[29]  Bo Du,et al.  Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion , 2019, IEEE Transactions on Cybernetics.

[30]  Bernt Schiele,et al.  Meta-Transfer Learning for Few-Shot Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Rita Strack,et al.  Deep learning in imaging , 2018, Nature Methods.

[32]  M. Huss,et al.  A primer on deep learning in genomics , 2018, Nature Genetics.

[33]  Gopalakrishnan Srinivasan,et al.  SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition , 2018, Front. Neurosci..

[34]  Xiong Luo,et al.  Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy , 2018, IEEE Transactions on Industrial Informatics.

[35]  Xin Wang,et al.  Mixture correntropy for robust learning , 2018, Pattern Recognit..

[36]  Crefeda Faviola Rodrigues,et al.  SyNERGY: An energy measurement and prediction framework for Convolutional Neural Networks on Jetson TX1 , 2018 .

[37]  Ahmad Reza Heravi,et al.  A New Correntropy-Based Conjugate Gradient Backpropagation Algorithm for Improving Training in Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Mariette Awad,et al.  Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework , 2018, Artif. Intell. Medicine.

[39]  Xudong Jiang,et al.  Deep Coupled ResNet for Low-Resolution Face Recognition , 2018, IEEE Signal Processing Letters.

[40]  Priyadarshini Panda,et al.  Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks , 2017, Front. Neurosci..

[41]  Satinder Singh,et al.  Artificial intelligence: Learning to play Go from scratch , 2017, Nature.

[42]  Daan Wierstra,et al.  Meta-Learning with Memory-Augmented Neural Networks , 2016, ICML.

[43]  Andrew S. Cassidy,et al.  Convolutional networks for fast, energy-efficient neuromorphic computing , 2016, Proceedings of the National Academy of Sciences.

[44]  Giacomo Indiveri,et al.  A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses , 2015, Front. Neurosci..

[45]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[46]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[47]  Andrew S. Cassidy,et al.  Merolla communication network and interface A million spiking-neuron integrated circuit with a scalable , 2014 .

[48]  Joachim M. Buhmann,et al.  Bagging for Path-Based Clustering , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Philip Goelet,et al.  The long and the short of long–term memory—a molecular framework , 1986, Nature.