Spiking Neural Network (SNN), as a brain-inspired machine learning algorithm, is closer to the computing mechanism of human brain and more suitable to reveal the essence of intelligence compared with Artificial Neural Networks (ANN), attracting more and more attention in recent years. In addition, the information processed by SNN is in the form of discrete spikes, which makes SNN have low power consumption characteristics .  In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks. In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding. Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness. Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Spiking Neural Network (SNN), as a brain-inspired machine learning algorithm, is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware. Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP) . But the previous SNNs trained through this approach are limited by slow learning speed or unsatisfactory learning capability. In this paper, to ease these limitations: 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature. 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding. 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning. We evaluated our contributions on the hand-written digit/letter classification tasks. The experimental results show that our Inception-like architecture exhibits superior performance than widely used Fully-Connected (FC) and Locally-Connected (LC) architectures. Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs , but also shows superior learning efficiency and robustness. Our SNN trained with only hundreds of iterations can achieve a favorable classification result, and randomly destructing network only leads to negligible performance degradation.
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closer to the computing mechanism of human brain and more suitable to reveal the essence of intelligence compared with Artificial Neural Networks (ANN), attracting more and more attention in recent years. In addition, the information processed by SNN is in the form of discrete spikes, which makes SNN have low power consumption characteristics
attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware. Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP)
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But the previous SNNs trained through this approach are limited by slow learning speed or unsatisfactory learning capability.
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we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks. In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the
to ease these limitations: 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature. 2) We extended the classic vote-based spike decoding scheme to a
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so as to reduce information loss during decoding. Simulation
to reduce the information loss in the spike decoding. 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning. We evaluated our contributions on the hand-written digit/letter classification tasks. The experimental
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BioSNet
our Inception-like architecture exhibits superior performance than widely used Fully-Connected (FC) and Locally-Connected (LC) architectures. Our SNN
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comparable results with the
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classification accuracy on MNIST/EMNIST data sets
SNNs
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exhibits
shows
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high robustness. Specifically, the BioSNet
robustness. Our SNN
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dozens of samples per class
hundreds of iterations
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accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet
result, and randomly destructing network
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slight
negligible