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Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Frontiers in Neuroscience, 2021 Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an ...
Simon Davidson, Steve B. Furber
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A biomimetic neural encoder for spiking neural network
Nature Communications, 2021 Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven ...
Shiva Subbulakshmi Radhakrishnan +4 more
semanticscholar +1 more source
Spiking Neural Network For Energy Efficient Learning And Recognition [PDF]
, 2020 Nowadays, people are confronted with an increasingly large amount of data and a tremendous change of human-machine interaction modes. It is a challenging and time-consuming task for traditional computing system to deal with the content of information ...
Wong, Yan Chiew, Wang, Ning Lo
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a-t-0/spiking-neural-network-of-dominating-set-approximation: v1.0.0
, 2021 <p>Neuromorphic computing is a promising new computational paradigm that may provide energy-lean solutions to algorithmic challenges such as graph problems.
Victoria Bosch +3 more
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Spiking neural network with local plasticity and sparse connectivity for audio classification [PDF]
Известия высших учебных заведений: Прикладная нелинейная динамикаPurpose. Studying the possibility of implementing a data classification method based on a spiking neural network, which has a low number of connections and is trained based on local plasticity rules, such as Spike-Timing-Dependent Plasticity.
Rybka, Roman Борисович +4 more
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Spiking Neural Networks and Their Applications: A Review
Brain Sciences, 2022 The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki +3 more
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A VLSI neuromorphic device for implementing spike-based neural networks [PDF]
, 2011 Indiveri G, Chicca E. A VLSI neuromorphic device for implementing spike-based neural networks. Presented at the Proceedings of the 21st Italian Workshop on Neural Nets (WIRN).We present a neuromorphic VLSI device which comprises hybrid analog/digital ...
Morabito, C. F. +5 more
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SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
Frontiers in Neuroscience, 2021 Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility.
Fang-Xin Liu +5 more
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Independent component analysis in spiking neurons [PDF]
, 2010 Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such ...
Cristina Savin +8 more
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A Layered Spiking Neural System for Classification Problems [PDF]
, 2022 Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving ...
Neri, Ferrante +6 more
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