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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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Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method
Frontiers in Neuroscience, 2022 Recent years witness an increasing demand for using spiking neural networks (SNNs) to implement artificial intelligent systems. There is a demand of combining SNNs with reinforcement learning architectures to find an effective training method.
Guanlin Wu +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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Brain-Inspired Computing: Models and Architectures
IEEE Open Journal of Circuits and Systems, 2020 With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machine learning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware.
Keshab K. Parhi, Nanda K. Unnikrishnan
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Spiking neural networks for computer vision [PDF]
Interface Focus, 2018 Abstract State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs.
Michael Hopkins +3 more
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Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications
Frontiers in Neuroscience, 2020 In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro +4 more
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Designing the Dynamics of Spiking Neural Networks [PDF]
Physical Review Letters, 2006 4 pages, 3 ...
Memmesheimer, R., Timme, M.
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Neuromorphic Sentiment Analysis Using Spiking Neural Networks
Sensors, 2023 Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their ...
Raghavendra K. Chunduri +1 more
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PlaNeural: Spiking Neural Networks that Plan [PDF]
Procedia Computer Science, 2016 PlaNeural is a spike-based neural network that has the ability to plan. The network is a spreading activation network implemented with Cell Assemblies; this combination has built a dynamic network of nodes that is able to interact with an environment and respond appropriately.
Ian Mitchell 0002 +2 more
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Financial time series prediction using spiking neural networks. [PDF]
PLoS ONE, 2014 In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction.
David Reid +2 more
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