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Neural Architecture Search for Spiking Neural Networks
, 2022 Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim +4 more
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An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method
Applied Sciences, 2022 Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal ...
Junqi Lu +4 more
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Building Logistic Spiking Neuron Models Using Analytical Approach
IEEE Access, 2019 Spiking neuron models are inspired by biological neurons. They can simulate the neuronal activities of the mammalian brains, such as spiking (integrator) and periodic oscillation (resonator).
Lei Zhang
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Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Frontiers in Neuroscience, 2021 Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an ...
Simon Davidson, Steve B. Furber
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Federated Learning With Spiking Neural Networks [PDF]
IEEE Transactions on Signal Processing, 2021 As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective,
Yeshwanth Venkatesha +3 more
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Deep learning in spiking neural networks [PDF]
Neural Networks, 2019 In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza +5 more
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Spiking Neural Networks and Their Applications: A Review
Brain Sciences, 2022 The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki +3 more
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Counting to Ten with Two Fingers: Compressed Counting with Spiking Neurons [PDF]
, 2019 We consider the task of measuring time with probabilistic threshold gates implemented by bio-inspired spiking neurons. In the model of spiking neural networks, network evolves in discrete rounds, where in each round, neurons fire in pulses in response to
Hitron, Yael, Parter, Merav
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Bifurcation Spiking Neural Network
, 2019 Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune.
Zhang, Shao-Qun +2 more
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Supervised Associative Learning in Spiking Neural Network [PDF]
, 2005 In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses
F. Crick +6 more
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