Spiking neural network (SNN)
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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A spiking neural network implementation of sound localisation
... Abstract – The focus of this paper is the implementation of a spiking neural network to achieve sound localization; the model is based on the influential short paper by Jeffress in 1948. The SNN has ...
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Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization
... Spiking Neural Network (SNN) falls into the third generation of Artificial Neural Networks ...Evolving Spiking Neural Network is one of the prominent SNN ...competent ...
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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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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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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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A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation
... a Spiking Neural Network (SNN) based model of the ...The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer ...
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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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FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware
... [1]. Spiking Neural Networks (SNNs) are a popular bio-inspired paradigm that have been used in many applications ...new Spiking Astrocyte-neuron Networks (SANNs) modulate the synaptic activities ...
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Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
... Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer ...in neural interface circuits and reduced computation ...Different spiking neuron ...
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Communication sparsity in distributed spiking neural network simulations to improve scalability
... In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) ...
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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
... artificial neural networks have been generally based on rate coding in the earliest stage of computational neuroscience ...of neural network performance and the main objective becomes how to encode ...
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FPGA Based Platform for Spiking Neural Network
... Spiking neural networks fall into third generation of neural network ...a neural simulation increasing the level of ...artificial neural networks (ANN), where neuron models ...
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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 ...a spiking neural network that would ...
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GPU-based implementation of real-time system for spiking neural networks
... A spiking neural network is a model of a real biological neural ...same spiking pattern unless a time step in synchronous system implementation is extremely small [1], ...
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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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Homeostatic Fault Tolerance in Spiking Neural Networks : A Dynamic Hardware Perspective
... A Spiking Neural Network (SNN) is a typical bio-inspired neural network that performs information transfer based on discrete-time ...
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Autonomous Learning Paradigm for Spiking Neural Networks
... Abstract. Compared to biological systems, existing learning systems lack the ability to learn autonomously, especially in changing and dy- namic environments. This paper addresses the issue of autonomous learn- ing by ...
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Effects of local network topology on the functional reconstruction of spiking neural network models
... We first look at counts of the dyadic transformations of both the transfer entropy and Gaussian reconstruction methods as a comparison of the two measures TE and CC. We ignore the trivial preservation transformation 1 → ...
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Multi-objective evolutionary algorithms of spiking neural networks
... evolving spiking neural network (ESNN) is used widely in recent ...efficient neural model and trained by a fast one-pass learning ...the network to evolve towards reaching the best ...
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