A learning-based approach to artificial sensory feedback leads to optimal integration.
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Intracortical Brain-Machine Interfaces Advance Sensorimotor NeuroscienceThe need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfacesReview of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal BreakdownBiological and bionic hands: natural neural coding and artificial perceptionToward an Integration of Deep Learning and NeuroscienceSensory augmentation: integration of an auditory compass signal into human perception of space.Dissecting neural circuits for multisensory integration and crossmodal processing.Human perception of electrical stimulation on the surface of somatosensory cortex.Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes.Qualitative assessment of patients' attitudes and expectations toward BCIs and implications for future technology development.Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons.Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex.Plasticity within non-cerebellar pathways rapidly shapes motor performance in vivo.Cortical neuroprosthetics from a clinical perspectiveBrain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.Task-Specific Somatosensory Feedback via Cortical Stimulation in Humans.Interfacing to the brain's motor decisions.Toward a Proprioceptive Neural Interface that Mimics Natural Cortical Activity.Trust in haptic assistance: weighting visual and haptic cues based on error history.Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces.Forward Prediction in the Posterior Parietal Cortex and Dynamic Brain-Machine InterfaceA Mixed-Signal VLSI System for Producing Temporally Adapting Intraspinal Microstimulation Patterns for Locomotion.The absence or temporal offset of visual feedback does not influence adaptation to novel movement dynamics.Cortical Neuroprosthesis Merges Visible and Invisible Light Without Impairing Native Sensory Function.Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings.Injecting Instructions into Premotor Cortex.Phantom Limbs, Neuroprosthetics, and the Developmental Origins of Embodiment.Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions.Illusory movement perception improves motor control for prosthetic hands.Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation.Engineering Artificial Somatosensation Through Cortical Stimulation in Humans.Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements
P2860
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P2860
A learning-based approach to artificial sensory feedback leads to optimal integration.
description
2014 nî lūn-bûn
@nan
2014 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
A learning-based approach to artificial sensory feedback leads to optimal integration.
@ast
A learning-based approach to artificial sensory feedback leads to optimal integration.
@en
type
label
A learning-based approach to artificial sensory feedback leads to optimal integration.
@ast
A learning-based approach to artificial sensory feedback leads to optimal integration.
@en
prefLabel
A learning-based approach to artificial sensory feedback leads to optimal integration.
@ast
A learning-based approach to artificial sensory feedback leads to optimal integration.
@en
P2860
P356
P1433
P1476
A learning-based approach to artificial sensory feedback leads to optimal integration.
@en
P2093
Joseph E O'Doherty
Maria C Dadarlat
P2860
P2888
P304
P356
10.1038/NN.3883
P407
P577
2014-11-24T00:00:00Z