Results 31 to 40 of about 3,555,912 (299)
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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A systematic method for configuring VLSI networks of spiking neurons [PDF]
, 2011 Neftci E, Chicca E, Indiveri G, Douglas RJ. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 2011;23(10):2457-2497.An increasing number of research groups are developing custom hybrid analog/digital very large ...
Rodney Douglas +11 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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Function approximation with uncertainty propagation in a VLSI spiking neural network [PDF]
, 2012 The brain combines and integrates multiple cues to take coherent, context-dependent action using distributed, event-based computational primitives. Computational models that use these principles in software simulations of recurrently coupled spiking ...
Sonnleithner, D. +20 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
CoRR, 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
openaire +5 more sources
Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition [PDF]
, 2009 Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology.
Douglas, R J +8 more
core +1 more source