Results 11 to 20 of about 68,095 (317)
Phase Diagram of Spiking Neural Networks [PDF]
Frontiers in Computational Neuroscience, 2014 In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2\%, 20\% of neurons are inhibitory and 80\% are excitatory.
Seyed-allaei, Hamed
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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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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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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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Event-based backpropagation can compute exact gradients for spiking neural networks
Scientific Reports, 2021 Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm ...
Timo C. Wunderlich, Christian Pehle
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A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA
IEEE Access, 2022 Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis.
Gianluca Leone +2 more
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Introducing the Dendrify framework for incorporating dendrites to spiking neural networks
Nature Communications, 2023 Biologically inspired spiking neural networks are highly promising, but remain simplified omitting relevant biological details. The authors introduce here theoretical and numerical frameworks for incorporating dendritic features in spiking neural ...
Michalis Pagkalos +2 more
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Multitask computation through dynamics in recurrent spiking neural networks
Scientific Reports, 2023 In this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks.
Mechislav M. Pugavko +2 more
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