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Spiking neural networks for nonlinear regression [PDF]
Royal Society Open Science, 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
Alexander Henkes +2 more
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Integrating Non-spiking Interneurons in Spiking Neural Networks [PDF]
Frontiers in Neuroscience, 2021 Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware.
Beck Strohmer +3 more
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Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm [PDF]
Jisuanji kexue, 2023 With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of ...
HUANG Zenan, LIU Xiaojie, ZHAO Chenhui, DENG Yabin, GUO Donghui
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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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Quantum superposition inspired spiking neural network [PDF]
iScience, 2021 Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared to human brain. Information in an artificial neural network (ANN) is represented using a statistical method and processed as a fitting function, enabling handling of ...
Yinqian Sun, Yi Zeng, Tielin Zhang
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Neural Spike Sorting Using Binarized Neural Networks [PDF]
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021 This article presents the design and efficient hardware implementation of binarized neural networks (BNNs) for brain-implantable neural spike sorting. In contrast to the conventional artificial neural networks (ANNs), in which the weights and activation functions of neurons are represented using real values, the BNNs utilize binarized weights and ...
Daniel Valencia, Amir Alimohammad
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Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework
Molecules, 2023 Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers.
Mauro Nascimben, Lia Rimondini
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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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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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Exploring the Connection Between Binary and Spiking Neural Networks
Frontiers in Neuroscience, 2020 On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Sen Lu, Abhronil Sengupta
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