Results 41 to 50 of about 46,112 (290)
Spikebench: An open benchmark for spike train time-series classification.
PLoS Computational Biology, 2023 Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially
Ivan Lazarevich +3 more
doaj +1 more source
NeuroImage, 2023 Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. Recently, neural decoding schemes, such as machine learning in functional magnetic resonance imaging (fMRI), has been employed to elucidate the ...
Noriya Watanabe +6 more
doaj +1 more source
Pruning Neural Belief Propagation Decoders [PDF]
2020 IEEE International Symposium on Information Theory (ISIT), 2020 This work was presented at the IEEE International Symposium on Information Theory (ISIT ...
Andreas Buchberger +4 more
openaire +3 more sources
Kernel Temporal Differences for Neural Decoding [PDF]
Computational Intelligence and Neuroscience, 2015 We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning ...
Jihye Bae +5 more
openaire +2 more sources
, 2009 The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported.
Ghassemlooy, Zabih +2 more
core +1 more source
Harmony search aided iterative channel estimation, multiuser detection and channel decoding for DS-CDMA [PDF]
, 2010 A novel Multiuser Detection (MUD) scheme is proposed for DS-CDMA systems employing the so-called Harmony Search (HS) algorithm, which is a novel meta-heuristic optimisation method.
Rong Zhang +3 more
core +1 more source
Neural speech decoding with magnetoencephalography
, 2022 Severe brain damage or amyotrophic lateral sclerosis (ALS) may lead the patients to a locked-in state where the patients are motorically paralyzed otherwise being cognitively normal. The brain might be the only source of communication for these patients.
Dash, Debadatta
core +1 more source
Improving the List Decoding Version of the Cyclically Equivariant Neural Decoder
2022 IEEE International Symposium on Information Theory (ISIT), 2022 The cyclically equivariant neural decoder was recently proposed in [Chen-Ye, International Conference on Machine Learning, 2021] to decode cyclic codes. In the same paper, a list decoding procedure was also introduced for two widely used classes of cyclic codes -- BCH codes and punctured Reed-Muller (RM) codes.
Xiangyu Chen, Min Ye 0005
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Trainable Greedy Decoding for Neural Machine Translation [PDF]
, 2017 Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-toend learning algorithms. The problem of decoding, however, has received relatively little attention from the research community.
Kyunghyun Cho +5 more
core +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source