Efficient event-based delay learning in spiking neural networks [PDF]
Nature CommunicationsSpiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks.
Balázs Mészáros +2 more
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BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification [PDF]
Scientific ReportsSpiking Neural Networks (SNNs), designed to more accurately model the brain’s neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational and ...
Kevin Takala +2 more
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Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks [PDF]
Scientific ReportsAccurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI).
Aaditya Joshi +4 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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Learning Universal Computations with Spikes. [PDF]
PLoS Computational Biology, 2016 Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the ...
Dominik Thalmeier +3 more
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Targeting operational regimes of interest in recurrent neural networks.
PLoS Computational Biology, 2023 Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons.
Pierre Ekelmans +2 more
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BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
Frontiers in Neuroinformatics, 2018 The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms.
Hananel Hazan +6 more
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