Results 21 to 30 of about 22,581 (258)
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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Models developed for spiking neural networks
MethodsX, 2023 Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared ...
Shahriar Rezghi Shirsavar +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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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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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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Bifurcation Spiking Neural Network
J. Mach. Learn. Res., 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.
Shao-Qun Zhang +2 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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Biomimetics, 2023 One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS).
William R. P. Nourse +3 more
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Modeling spiking neural networks
Theoretical Computer Science, 2008 AbstractA notation for the functional specification of a wide range of neural networks consisting of temporal or non-temporal neurons, is proposed. The notation is primarily a mathematical framework, but it can also be illustrated graphically and can be extended into a language in order to be automated. Its basic building blocks are processing entities,
Ioannis D. Zaharakis, Achilles D. Kameas
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