Results 21 to 30 of about 38,728 (269)
Crystals, 2021 A synaptic device that contains weight information between two neurons is one of the essential components in a neuromorphic system, which needs highly linear and symmetric characteristics of weight update.
Minkyung Kim +10 more
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Martingales and the characteristic functions of absorption time on bipartite graphs
Royal Society Open Science, 2021 Evolutionary graph theory investigates how spatial constraints affect processes that model evolutionary selection, e.g. the Moran process. Its principal goals are to find the fixation probability and the conditional distributions of fixation time, and ...
Travis Monk, André van Schaik
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Neuromorphic Silicon Neuron Circuits [PDF]
Frontiers in Neuroscience, 2011 ISSN:1662 ...
Indiveri, Giacomo +19 more
openaire +11 more sources
Neuromorphic Learning towards Nano Second Precision [PDF]
, 2013 Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival
Meier, Karlheinz +3 more
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Frontiers in Neuromorphic Engineering [PDF]
Frontiers in Neuroscience, 2011 ISSN:1662 ...
Indiveri, G, Horiuchi, T K
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Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In recent years, deep neural networks have shown to achieve state-of-the-art performance on several classification and prediction tasks. However, these networks demand undesirable lengthy training times coupled with high computational resources (memory ...
Humza Syed +3 more
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Energy-Efficient Neuromorphic Classifiers [PDF]
Neural Computation, 2016 Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of ...
MartÃ, Daniel +3 more
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Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades [PDF]
, 2015 Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labelling existing ...
Ajinkya eJayawant +4 more
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On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor [PDF]
, 2019 Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).
Neftci, Emre +3 more
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Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 more
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