Spiking neural networks
Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
... of neural networks that are currently under focus by the Artificial Intelligence ...Deep Neural Networks (DNNs), based on traditional, non- spiking, feed-forward neural ...
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A CMOS based Neuron Circuit for Spiking Neural Networks with Memristive Synapse
... brained-inspired spiking neural networks (SNNs), which can be applied on visual information processing and speech recognition, is attracting great attention especially when combined with emerging ...
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A Spiking Neural Networks Based Face Recognition Algorithm
... noise. Spiking neural networks, which inherit the parallel mechanism from biological system, are used to extract the face ...The spiking neural networks can remember key features ...
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Training deep spiking neural networks using backpropagation
... Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based ...such networks is ...
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Efficient computation in adaptive artificial spiking neural networks
... Artificial Neural Networks (ANNs) are bio-inspired models of neural computa- tion that have proven highly ...and neural units in ANNs exchange analog values in a frame-based manner, a ...
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Direct Training for Spiking Neural Networks: Faster, Larger, Better
... Spiking neural networks (SNNs) that enables energy effi- cient implementation on emerging neuromorphic hardware are gaining more ...artificial neural net- works (ANNs), due to the lack of ...
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Solving the Linearly Inseparable XOR Problem with Spiking Neural Networks
... Abstract—Spiking Neural Networks (SNN) are third generation neural networks and are considered to be the most biologically plausible so ...using networks of spiking ...a ...
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The Evolution of Training Parameters for Spiking Neural Networks with Hebbian Learning
... and Spiking Neural Networks SNNs are the third and most recent generation of NNs and were inspired by the firing paradigm of the biological ...
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GPU-based implementation of real-time system for spiking neural networks
... biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and ...
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A survey on real time processing with spiking neural networks
... The spiking neural network architecture (SpiNNaker) inspired by the mammalian brain is a massively parallel million core architecture that aims to model real time large-scale spiking neural ...
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Evolving spiking neural networks for spatio and spectro-temporal pattern recognition
... brain-inspired Spiking Neural Networks (SNN) have the potential to learn SSTD by using trains of spikes (binary temporal events) transmitted among spatially located synapses and ...their ...
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Evolution of Spiking Neural Networks for Temporal Pattern Recognition and Animat Control
... regulatory networks for tasks includ- ing controlling single cells, as unicellular entities or parts of multicellular bodies in two dimensions [80, 81] (it has been used to transform the structures into ...
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Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures
... Abstract—Recent studies have shown that the dielectric properties of normal breast tissue vary considerably. This dielectric heterogeneity may mean that the identification of tumours using Ultra Wideband Radar imaging ...
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Neuromorphic audio processing through real-time embedded spiking neural networks.
... for spiking neural networks simulation with or without learning; or jAER [9], for real-time visualiza- tion and software processing of AER streams captured from the hard- ware using speci fi ed ...
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Application of spiking neural networks and the bees algorithm to control chart pattern recognition
... The mammalian brain contains more than 1010 densely packed neurons that are connected to an intricate network. In every small volumes o f cortex, thousands o f spikes are emitted each millisecond. Generally, it is agreed ...
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Spatio-/spectro-temporal pattern recognition using evolving probabilistic spiking neural networks
... brain-inspired spiking neural networks (SNN) (Gerstner & Kistler, 2002b; Maass & Zador, 1999), are considered the third generation of neural ...
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Speech emotion recognition with early visual cross modal enhancement using spiking neural networks
... speech. Spiking Neural Networks (SNN) have demonstrated as a promising approach in machine learning and pattern recognition tasks such as handwriting and facial expression ...
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Multi-objective evolutionary algorithms of spiking neural networks
... Spiking neural networks (SNNs), the third generation of ANNs, play an essential role in biological information processing (Gerstner and Kistler, ...representation, spiking models provide an ...
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Unsupervised learning in an ensemble of spiking neural networks mediated by ITDP
... of spiking neural ...SEM networks whose outputs are combined via an ITDP based ...our spiking ensemble system according to the observations on diversity described in the previous ...
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Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
... Adaptive Spiking Neural Networks (ASNNs) through drop-in replacement of ReLU neurons in standard ReLU-based feedforward and convolu- tional ANNs, and we demonstrate identical performance to these ...
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