Spiking neural network
Spiking Neural Network on Curve Fitting
... artificial neural networks are widely used in information science, brain discipline, neuron psychology and other ...Artificial neural networks [1,2] (ANN) are a model that processes distributed parallel ...
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Deep convolutional spiking neural network based hand gesture recognition
... convolutional spiking neural network and we have evaluated it with our high-density sEMG dataset (Strathclyde dataset) and another open source sEMG dataset (CapgMyo ...and network training ...
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Receptive field optimisation and supervision of a fuzzy spiking neural network
... a spiking neural ...fuzzy spiking neural network (FSNN) is representative of a fuzzy rule ...the network is supervised but learning only occurs locally as in the biological ...
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ACCELERATION OF SPIKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS
... a spiking neural network based character recognition model on three NVIDIA GPGPU platforms and one NVIDIA GPGPU cluster: the GeForce 9800 GX2, the Tesla C1060, the Tesla S1070 platforms, and the 32 ...
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Integrated feature and parameter optimization for an evolving spiking neural network
... Evolving Spiking Neural Network (ESNN) ar- chitecture on the Two-Spiral problem firstly introduced in ...for neural networks, including the analysis of the ESNN method itself ...
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Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker
... modelling spiking neural networks with ...multilayer spiking neural network for audio samples classification using SpiNNaker is ...The network consists of different leaky ...
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Configuring spiking neural network training algorithms
... of spiking neural networks comes at a ...second-generation neural network, with the backprop- agation algorithm being the gold standard, the question of training a spiking neural ...
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Spiking neural network model of sound localisation using the interaural intensity difference
... a spiking neural network (SNN) architecture to simulate the sound localisation ability of the mammalian auditory pathways using the interaural intensity difference (IID) cue is ...inhibitory ...
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Application of a brain-inspired spiking neural network architecture to odor data classification
... the spiking data for identification of odor, remain largely ...brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data ...
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Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization
... every neural network there are parameters involved and some approaches are employed for parameter setting such as manual tuning or an automated process using an optimizer (Silva et ...Evolving ...
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Fast flux botnet detection based on adaptive dynamic evolving spiking neural network
... Service Network (FFSN) is a special type of botnet that has been engaged by bot herders to cover malicious botnet activities, and increase the lifetime of malicious servers by quickly changing the IP addresses of ...
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NatCSNN: a convolutional spiking neural network for recognition of objects extracted from natural images
... complex neural networks composed of thousands of neurons interconnected via thou- sands of synapses, some of which are excitatory and others ...inhibitory. Spiking neural models are distinguished ...
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Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network
... employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various ...
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Evolving Spiking Neural Network Topologies for Breast Cancer Classification in a Dielectrically Heterogeneous Breast
... In this paper, a novel Spiking Neural Network (SNN) classifier is presented and examined. SNNs [16] emulate biological neurons and aim to replicate the brains ability to function well when presented ...
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A spiking neural network implementation of sound localisation
... a spiking neural network will implement the Jeffress sound localization technique for a limited number of angles in the azimuthal ...the network and the output neurons will determine the angle ...
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Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
... the winner neurons (first spiking neurons) are increased while other neurons suffer a small weight reduction value. Tests showed that depressing the weights of the non firing neurons decrease the amount of noise ...
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Evolving spiking neural network - a survey
... The two models deSNN and reSNN, designed towards the processing of spatio-temporal real world data, appear es- pecially interesting. Future studies will focus on the tighter integration of reservoir based techniques with ...
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UAV detection : a STDP trained deep convolutional spiking neural network retina-neuromorphic approach
... ing Neural Network (SNN) trained using the unsupervised approach of Spike Time-Dependent Plasticity (STDP), to create an asynchronous, low power system with low computational ...the network, this ...
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Effect of the Neuron Coding by Gaussian Receptive Fields on Enhancing the Performance of Spiking Neural Network for an Automatic Lipreading System
... The spiking neural networks are able, by definition, to receive and treat an input spike train to emit another output spike train. By analogy with the conventional versions, the plasticity rule defined the ...
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FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware
... effect of under-sampling for biology time of 100,000 seconds. For example, if every one in ten samples are recorded, the overall simulation and acquisition time will be reduced from around 724 seconds to ~63 seconds. ...
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