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The Neural Decoding Toolbox [PDF]
Frontiers in Neuroinformatics, 2013 Population decoding is a powerful way to analyze neural data, however currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (
Ethan eMeyers
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Machine Learning for Neural Decoding. [PDF]
eNeuro, 2020 AbstractDespite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance.
Glaser JI +5 more
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Robust neural decoding with low-density EEG [PDF]
Scientific ReportsHigh-density Electroencephalography (EEG) recording enhances spatial resolution for neural signal decoding, yet the relationship between electrode density and decoding performance remains unclear.
Ling Huang +2 more
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Towards in vivo neural decoding. [PDF]
Biomed Eng Lett, 2022 Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computing device, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain's activity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in ...
Valencia D, Alimohammad A.
europepmc +3 more sources
Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding [PDF]
Journal of NeuroEngineering and RehabilitationAs a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims
Kangchen Li +10 more
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Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission [PDF]
Brain SciencesBackground: In brain–computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are ...
Jiaqi Zheng +7 more
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Decoding Methods in Neural Language Generation: A Survey
Information, 2021 Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding ...
Sina Zarrieß +2 more
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Decoding surface code with a distributed neural network–based decoder [PDF]
Quantum Machine Intelligence, 2020 AbstractThere has been a rise in decoding quantum error correction codes with neural network–based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space.
Savvas Varsamopoulos +2 more
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Hypernetwork Based Model-Driven Channel Neural Decoding
IEEE AccessChannel decoding algorithms based on model-driven deep learning, also known as channel neural decoding algorithms, have received a lot of attention in recent years.
Yuanhui Liang +4 more
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An EEG Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening [PDF]
Scientific DataEEG-based neural decoding requires large-scale benchmark datasets. Paired brain-language data across speaking, listening, and reading modalities are essential for aligning neural activity with the semantic representation of large language models (LLMs ...
Sitong Chen +10 more
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