Results 11 to 20 of about 28,095 (262)
Spark: modular spiking neural networks
Frontiers in Artificial IntelligenceNowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy.
Mario Franco, Carlos Gershenson
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Quantization in Spiking Neural Networks
CoRR, 2023 arXiv admin note: text overlap with arXiv:2305 ...
Bernhard Alois Moser, Michael Lunglmayr
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Expressivity of Spiking Neural Networks
CoRR, 2023 The synergy between spiking neural networks and neuromorphic hardware holds promise for the development of energy-efficient AI applications. Inspired by this potential, we revisit the foundational aspects to study the capabilities of spiking neural networks where information is encoded in the firing time of neurons.
Manjot Singh +2 more
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Attention Spiking Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 18 pages, 8 figures, Under ...
Man Yao +7 more
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Stochasticity and robustness in spiking neural networks [PDF]
Neurocomputing, 2021 Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artificial networks which utilize analog 'synaptic' devices to encode weights, however, inherent limits are placed on the accuracy and precision with which these values can be ...
Wilkie Olin-Ammentorp +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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Spiking Neural Networks: A Survey
IEEE Access, 2022 The field of Deep Learning (DL) has seen a remarkable series of developments with increasingly accurate and robust algorithms. However, the increase in performance has been accompanied by an increase in the parameters, complexity, and training and inference time of the models, which means that we are rapidly reaching a point where DL may no longer be ...
João D. Nunes +3 more
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Agreement in Spiking Neural Networks
Journal of Computational Biology, 2022 We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O(n) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O(logn) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-
Kunev, Martin +2 more
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IEEE Access, 2022 In recent years, the use of artificial neural network applications to perform object classification and event prediction has increased, mainly from research about deep learning techniques running on hardware such as GPU and FPGA.
Francisco De Assis Pereira Januario +1 more
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Spiking Neural Networks for Nonlinear Regression [PDF]
Proceedings of the Neuromorphic Materials, Devices, Circuits and Systems, 2023 Spiking neural networks (SNN), also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over traditional, second-generation neural networks. Inspired by the undisputed efficiency of the human brain, they introduce temporal and neuronal sparsity, which can be ...
Alexander Henkes +2 more
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