Deep learning approaches for neural decoding across architectures and recording modalities
JA Livezey, JI Glaser - Briefings in bioinformatics, 2021 - academic.oup.com
Decoding behavior, perception or cognitive state directly from neural signals is critical for
brain–computer interface research and an important tool for systems neuroscience. In the …
brain–computer interface research and an important tool for systems neuroscience. In the …
Real-time multimodal sensory detection using widefield hippocampal calcium imaging
The hippocampus is a complex structure that has a major role in learning and memory. It
also integrates information from multisensory modalities, supporting a comprehensive …
also integrates information from multisensory modalities, supporting a comprehensive …
[HTML][HTML] Forecasting immune effector cell-associated neurotoxicity syndrome after chimeric antigen receptor t-cell therapy
Background Immune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical
and neuropsychiatric syndrome that can occur days to weeks following administration …
and neuropsychiatric syndrome that can occur days to weeks following administration …
Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model
MR Rezaei, H Jeoung, A Gharamani… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial
prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives …
prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives …
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
JA Livezey, JI Glaser - arXiv preprint arXiv:2005.09687, 2020 - arxiv.org
Decoding behavior, perception, or cognitive state directly from neural signals has
applications in brain-computer interface research as well as implications for systems …
applications in brain-computer interface research as well as implications for systems …
Direct discriminative decoder models for analysis of high-dimensional dynamical neural data
With the accelerated development of neural recording technology over the past few
decades, research in integrative neuroscience has become increasingly reliant on data …
decades, research in integrative neuroscience has become increasingly reliant on data …
Reverse survival model (RSM): a pipeline for explaining predictions of deep survival models
The aim of survival analysis in healthcare is to estimate the probability of occurrence of an
event, such as a patient's death in an intensive care unit (ICU). Recent developments in …
event, such as a patient's death in an intensive care unit (ICU). Recent developments in …
Alternators For Sequence Modeling
This paper introduces alternators, a novel family of non-Markovian dynamical models for
sequences. An alternator features two neural networks: the observation trajectory network …
sequences. An alternator features two neural networks: the observation trajectory network …
Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
The state-space models (SSMs) are widely utilized in the analysis of time-series data. SSMs
rely on an explicit definition of the state and observation processes. Characterizing these …
rely on an explicit definition of the state and observation processes. Characterizing these …
[PDF][PDF] 人工智能在动物实验中应用的研究进展
王星， 赵静怡， 张钰， 沈璐妍 - 中国比较医学杂志, 2022 - zgbjyx.cnjournals.com
实验动物伦理与3R 原则是动物实验过程中必须遵守的基本准则. 但由于动物机体的复杂性,
动物体内实验所得到的各种数据面临着庞大而没有合适方法充分挖掘, 复杂而有很多隐藏信息 …
动物体内实验所得到的各种数据面临着庞大而没有合适方法充分挖掘, 复杂而有很多隐藏信息 …