spiking artificial neural networks
Efficient computation in adaptive artificial spiking neural networks
... Abstract. 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 ...
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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 ...of artificial learning, SNNs are still ...
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A survey on real time processing with spiking neural networks
... neurons. Spiking neural network (SNN) approach in ANNs is more closely related to the biological neurons [7] with information encoding in the form of action potentials called spikes that last for one ...
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Multi-objective evolutionary algorithms of spiking neural networks
... classifiers, artificial neural network (ANN) classifiers have proved to be one of the most robust classification systems; their ability to deal with noisy input patterns and to handle both noisy and ...
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Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
... Adaptive Spiking Neuron To create artificial spiking neural networks based on sigma-delta spike-time coding, we address the limited dynamic range of standard LIF or corresponding ...
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Feature Extraction using Spiking Convolutional Neural Networks
... Spiking neural networks are biologically plausible counter- parts of the artificial neural networks, artificial neural net- works are usually trained with ...
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A review of learning in biologically plausible spiking neural networks
... The Liquid State Machine (LSM) provides an approach that consists of a dynamic filter. The dynamic filter is constructed by a recurrent SNN called reservoir. It maps input spike trains to an internal dynamic state which ...
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Surrogate Gradient Learning in Spiking Neural Networks
... a spiking neuron non-linearity was approximated by the derivative of a truncated quadratic function, thus resulting in a rectifying linear unit (ReLU) as surrogate derivative ...binary neural ...
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Computational modeling with spiking neural networks
... neurons. Spiking neurons represent the third generation of neural models, incorporating the concepts of time, neural, and synaptic state explicitly into the model ...Earlier artificial ...
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Computing with spiking neuron networks a review
... pulses. Artificial Neural Networks (ANN) try to capture the essence of this computation as depicted in figure ...for neural networks prescribe how to adapt the weights to improve ...
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A Spiking Neural Networks Based Face Recognition Algorithm
... The spiking neural networks, which inherit the parallel mechanism from biological system, are introduced to extract the features of the face ...to spiking neuron based artificial ...
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Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
... A spiking neural network (SNN) is an artificial neural network mimics neural networks from biology [8]. In a SNN, every neuron has a threshold and a refractory period. As opposed ...
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Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
... Spiking neural networks are considered to be the third generation of artificial neural networks (ANN). While classic ANN operate with real or integer-valued inputs, SNN process ...
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Type of Tomato Classification Using Deep Learning
... Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain ...
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Prediction of Compressive Strength of Concrete using Artificial Neural Network
... propagation and Jordan Elman back propagation algorithms are used to adjust the connection weights and bias values training. The network parameters tested in the proposed model included the following: training data = ...
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Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
... in neural networks, ESNN has received a lot of attention since ESNN offers several advantages over other neural networks model such as perceptron and multilayer perceptron (MLP) (Batllori et ...
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Analysis of cardiovascular (cvd)/coronary heart diseases(chd) using artificial neural network (ann)
... the neural network refers to a network of biological neurons. Artificial neural network (ANN) is the mimicking of the human neuron on a ...neurons.[1][2]. Artificial neural ...
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A Study on Effective Algorithm for Medical Decision Making System
... (NDT), Artificial neural networks could be used in every situation in which exists a relationship between some variables that can be considered inputs and other variables that can be ...
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Training deep spiking neural networks using backpropagation
... deep neural networks, which can be applied to train deep ...optimize networks by using real spike events from neuromorphic vision sensors during ...
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Therapeutic applications of computer models of brain activity for Alzheimer disease.
... Neural networks and other computational intelligence models inspired by our under- standing of the brain are widely used for medical diagnostics support, signal and image analysis, monitoring, search for ...
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