Results 31 to 40 of about 68,095 (317)
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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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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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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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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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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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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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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, 2017 Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural ...
Aimone, James B. +9 more
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Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 more
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