spiking neural network architecture
Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture
... Table 2 summarises the classification accuracy achieved from the proposed SNN method while Table 3 represents the results of conventional learning techniques. In Table 2 a confusion table is reported to show the ...
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Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
... simplified spiking neuron architecture optimized for embedded systems implemen- tation, proving the learning capabilities of the ...The network preserves its learning and classification prop- erties ...
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Application of a brain-inspired spiking neural network architecture to odor data classification
... The spiking information is propagated through the 3D SNNr for deep learning and classification ...the network, and an unsupervised learning method, such as STDP, is implemented resulting in modifications of ...
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Integrated feature and parameter optimization for an evolving spiking neural network
... use Spiking Neural Networks (SNN) for solving practical real world ...similar spiking neural model was analyzed [7], in which a classification problem for taste recognition was ...efficient ...
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Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network
... evolving spiking neural networks (eSNN), which has been represented by the NeuCube architecture was introduced for visualization, dynamic learning, and classification of fMRI as spatiotemporal brain ...
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Spiking neural network model of sound localisation using the interaural intensity difference
... the architecture of the mammalian auditory pathways; a wide range of sound frequencies to test the localisation ability of the architecture; the utilisation of real experimental data rather than simulated ...
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Brain Disease Detection From EEGS: Comparing Spiking and Recurrent Neural Networks for Non-stationary Time Series Classification
... One particularly intriguing advance made in AI is the development of recurrent ANN modelling techniques, such as the Recurrent Neural Network (RNN). Early ANNs were exclusively feed-forward, meaning that ...
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Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker
... SpiNNaker is a massively-parallel multi-core computing system designed for modelling very large spiking neural networks in real time. Each SpiNNaker chip comprises 18 general-purpose ARM968 cores, running ...
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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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ACCELERATION OF SPIKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS
... As shown in Fig 4.2, the three basic components of the GPGPU cluster utilized in this study are the host nodes, the GPGPUs and the interconnect between the host nodes. The GPGPU cluster being used in this study is the ...
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A comparison of sound localisation techniques using cross correlation and spiking neural networks for mobile robotics
... a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile ...SNN architecture aims to simulate the sound localisation ...
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Online Full Text
... new architecture for spiking neural networks for wood defect classification The proposed architecture consists of a feed forward network of spiking neurons which is fully ...
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Architectures and Design of VLSI Machine Learning Systems
... (DNP) architecture for large-scale spiking neural networks is presented in [34]- [48], which supports spike timing dependent plasticity (STDP) learning ...any neural network topologies ...
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Evolving spiking neural network - a survey
... The eSNN extension presented in [61] combines a bi- nary optimization algorithm, namely the Versatile Quantum- inspired Evolutionary Algorithm (vQEA) [9], with an eSNN. vQEA is a probabilistic approach in which a num- ...
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Title: DIABETIC RETINOPATHY DETECTION USING DEEP NEURAL NETWORK
... Abstract– Diabetic Retinopathy (die-uh-BET-ik ret-ih-Nop-uh-thee) is a diabetic complication that effects eyes. It is caused by damage to the blood vessels of the light – sensitive tissues at the back of eye (retina). ...
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Communication sparsity in distributed spiking neural network simulations to improve scalability
... ARCHER allocates exclusive computing nodes (cores and memory), however, as a cluster computer, network related resources are potentially shared. There are two types of noise that can affect benchmarking results: ...
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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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Multi-objective evolutionary algorithms of spiking neural networks
... 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 continuous data ...
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A spiking neural network for real-time Spanish vowel phonemes recognition
... the spiking digital cochlea. Fig. 6 shows the output histogram of the spiking digital cochlea in response to the swept-sine wave from 200 Hz to 1200 ...
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Vol 5, No 07 (2017)
... models neural inputs are combined using the summing ...multiplicative neural networks that contain units which multiply their inputs instead of summing them is used and this, allows inputs to interact ...
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