Searched for: subject%3A%22Spiking%255C%2BNeural%255C%2BNetworks%255C%2528SNNs%255C%2529%255C%2529%22
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Huang, Jiongyu (author)
A Spiking neural network (SNN) is a type of artificial neural network which encodes information using spike timing, network structure, and synaptic weights to emulate the information processing function of the human brain. Within an SNN, it is always required to support the spike transmission that travels between neurons(array). This thesis aims...
master thesis 2023
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Zhou, Yongkang (author)
Spiking neural networks (SNN), as the third-generation artificial neural network, has a similar potential pulse triggering mechanism to the biological neuron. This mechanism enables the spiking neural network to increase computing power compared to the traditional artificial neural network to process complex information. However, a large number...
master thesis 2022
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LU, Jingyi (author)
Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...
master thesis 2022
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Stroobants, S. (author), Dupeyroux, J.J.G. (author), de Croon, G.C.H.E. (author)
ompelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and...
journal article 2022
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Stroobants, S. (author), Dupeyroux, J.J.G. (author), de Croon, G.C.H.E. (author)
The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using...
conference paper 2022
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de Gelder, Luuk (author)
Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited...
master thesis 2021
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van Wezel, Martijn (author)
Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, and are inherently more low-power than other Artificial Neural...
master thesis 2020
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Büller, Bas (author)
Spiking neural networks are notoriously hard to train because of their complex dynamics and sparse spiking signals. However, in part due to these properties, spiking neurons possess high computa- tional power and high theoretical energy efficiency. This thesis introduces an online, supervised, and gradient-based learning algorithm for spiking...
master thesis 2020
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Joshi, Ninad (author)
Traditional Artificial Neural Networks(ANNs)like CNNs have shown tremendous opportunities in various domains like autonomous cars, disease diagnosis, etc. Proven learning algorithms like backpropagation help ANNs in achieving higher accuracy. But there is a serious challenge with the increasing popularity of traditional ANNs is of energy...
master thesis 2019
Searched for: subject%3A%22Spiking%255C%2BNeural%255C%2BNetworks%255C%2528SNNs%255C%2529%255C%2529%22
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