Applications of deep learning and reinforcement learning to biological data
Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …
new possibilities for life scientists to gather multimodal data in various application domains …
Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning
BT Marsh, VSA Tarigoppula, C Chen… - Journal of …, 2015 - Soc Neuroscience
For decades, neurophysiologists have worked on elucidating the function of the cortical
sensorimotor control system from the standpoint of kinematics or dynamics. Recently …
sensorimotor control system from the standpoint of kinematics or dynamics. Recently …
Neuroplasticity of the sensorimotor cortex during learning
JT Francis, W Song - Neural plasticity, 2011 - Wiley Online Library
We will discuss some of the current issues in understanding plasticity in the sensorimotor
(SM) cortices on the behavioral, neurophysiological, and synaptic levels. We will focus our …
(SM) cortices on the behavioral, neurophysiological, and synaptic levels. We will focus our …
Neural control of a tracking task via attention-gated reinforcement learning for brain-machine interfaces
Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn
from the environment through interactions to complete the task without desired signals …
from the environment through interactions to complete the task without desired signals …
Quantized attention-gated kernel reinforcement learning for brain–machine interface decoding
F Wang, Y Wang, K Xu, H Li, Y Liao… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret
dynamic neural activity without patients' real limb movements. In conventional RL, the goal …
dynamic neural activity without patients' real limb movements. In conventional RL, the goal …
Neural decoders using reinforcement learning in brain machine interfaces: A technical review
B Girdler, W Caldbeck, J Bae - Frontiers in Systems Neuroscience, 2022 - frontiersin.org
Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of
research that has been explored for decades in medicine, engineering, commercial, and …
research that has been explored for decades in medicine, engineering, commercial, and …
Clustering neural patterns in kernel reinforcement learning assists fast brain control in brain-machine interfaces
X Zhang, C Libedinsky, R So… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Neuroprosthesis enables the brain control on the external devices purely using neural
activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the …
activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the …
Reward expectation modulates local field potentials, spiking activity and spike-field coherence in the primary motor cortex
Reward modulation (M1) could be exploited in developing an autonomously updating brain-
computer interface (BCI) based on a reinforcement learning (RL) architecture. For an …
computer interface (BCI) based on a reinforcement learning (RL) architecture. For an …
Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by
modulating neural activity. This can be potentially used to mitigate the damage of brain …
modulating neural activity. This can be potentially used to mitigate the damage of brain …
Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm
Embedding computational models in the physical world is a critical step towards
constraining their behavior and building practical applications. Here we aim to drive a …
constraining their behavior and building practical applications. Here we aim to drive a …