Results 61 to 70 of about 2,367,946 (319)
Speech synthesis from neural decoding of spoken sentences
Nature, 2019 Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments.
G. Anumanchipalli +2 more
semanticscholar +1 more source
Neural decoding of speech with semantic-based classification.
Cortex; a journal devoted to the study of the nervous system and behavior, 2022 Speech is a complex cognitive process that begins with conceptualization, proceeds to word-level processing, and ends with articulation. Neural decoding of speech (i.e., using neural activity to decode the content of language production) has been mostly ...
Yi Lin, P. Hsieh
semanticscholar +1 more source
SORN : a self-organizing recurrent neural network [PDF]
, 2009 Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing ...
Pipa, Gordon +5 more
core +1 more source
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
De Bruijn cycles for neural decoding [PDF]
Journal of Vision, 2011 Stimulus counterbalance is critical for studies of neural habituation, bias, anticipation, and (more generally) the effect of stimulus history and context. We introduce de Bruijn cycles, a class of combinatorial objects, as the ideal source of pseudo-random stimulus sequences with arbitrary levels of counterbalance. Neuro-vascular imaging studies (such
Geoffrey Karl Aguirre +2 more
openaire +2 more sources
Recurrent Neural Network Encoding Decoding Translator based Prediction Protein Function and Functional Annotation [PDF]
, 2021 Protein sequences are symbols generally different characters representing the 20 amino acids used in human proteins those sequences can range from the very sort to the very long.
Islam, MM +5 more
core +1 more source
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
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
Journal of Neural Engineering, 2021 Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms.
H. Yokoyama +3 more
semanticscholar +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