ArticleFrontiers in neuroscience2016
Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Calibrating Bayesian Decoders of Neural Spiking Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks.Frontiers in neuroscience · 2022Article
- Rapid and transient enhancement of thalamic information transmission induced by vagus nerve stimulation.Journal of neural engineering · 2020Article
- State-aware detection of sensory stimuli in the cortex of the awake mouse.PLoS computational biology · 2019Article
- Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects.Experimental neurobiology · 2018Review
- Intracortical Microstimulation Modulates Cortical Induced Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- State-dependent representation of stimulus-evoked activity in high-density recordings of neural cultures.Scientific reports · 2018Article
- Decoding English Alphabet Letters Using EEG Phase Information.Frontiers in neuroscience · 2018Article
- State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.Frontiers in neuroscience · 2017Article
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Authors and funding
4 authors.
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Abstract
Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.
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