Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions

Neuron, Journal Year: 2020, Volume and Issue: 107(2), P. 351 - 367.e19

Published: May 19, 2020

Language: Английский

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Power-saving design opportunities for wireless intracortical brain–computer interfaces

Nature Biomedical Engineering, Journal Year: 2020, Volume and Issue: 4(10), P. 984 - 996

Published: Aug. 3, 2020

Language: Английский

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Ventral Tegmental Dopamine Neurons Control the Impulse Vector during Motivated Behavior

Current Biology, Journal Year: 2020, Volume and Issue: 30(14), P. 2681 - 2694.e5

Published: May 28, 2020

Language: Английский

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An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: March 9, 2020

Abstract Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline analysis with different models and conditions to assess how they influence performance stability decoder. Specifically, conducted three experiments that accuracy: (1) delta band time-domain features, (2) when downsampling data, (3) frequency features. each experiment, eight decoder algorithms were compared including current state-of-the-art. Different tap sizes (sample window sizes) also evaluated a real-time applicability assessment. A feature importance was ascertain which features most relevant decoding; moreover, perturbations assessed quantify robustness methods. Results indicated generally Gated Recurrent Unit (GRU) Quasi Neural Network (QRNN) outperformed other methods in terms accuracy stability. state-of-the-art Unscented Kalman Filter (UKF) still decoders using smaller sizes, fast convergence performance, but occurred at cost noise vulnerability. Downsampling inclusion yielded overall improvement performance. The results suggest neural network-based or wide range could not only improve applications stable use BCIs.

Language: Английский

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Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 12, 2022

Abstract Despite the rapid progress and interest in brain-machine interfaces that restore motor function, performance of prosthetic fingers limbs has yet to mimic native function. The algorithm converts brain signals a control signal for device is one limitations achieving realistic finger movements. To achieve more movements, we developed shallow feed-forward neural network decode real-time two-degree-of-freedom movements two adult male rhesus macaques. Using two-step training method, recalibrated feedback intention–trained (ReFIT) introduced further improve performance. In 7 days testing across animals, decoders, with higher-velocity natural appearing achieved 36% increase throughput over ReFIT Kalman filter, which represents current standard. decoders herein demonstrate decoding continuous at level superior state-of-the-art could provide starting point using networks development naturalistic brain-controlled prostheses.

Language: Английский

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The science and engineering behind sensitized brain-controlled bionic hands

Physiological Reviews, Journal Year: 2021, Volume and Issue: 102(2), P. 551 - 604

Published: Sept. 20, 2021

Advances in our understanding of brain function, along with the development neural interfaces that allow for monitoring and activation neurons, have paved way brain-machine (BMIs), which harness signals to reanimate limbs via electrical muscles or control extracorporeal devices, thereby bypassing senses altogether. BMIs consist reading out motor intent from neuronal responses monitored regions executing intended movements bionic limbs, reanimated exoskeletons. also restoration sense touch by electrically activating neurons somatosensory brain, evoking vivid tactile sensations conveying feedback about object interactions. In this review, we discuss mechanisms somatosensation able-bodied individuals describe approaches use as movement activate residual sensory pathways restore touch. Although focus review is on intracortical approaches, alternative signal sources noninvasive strategies restoration.

Language: Английский

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Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Aug. 15, 2022

The spiking activity of populations cortical neurons is well described by the dynamics a small number population-wide covariance patterns, whose activation we refer to as ‘latent dynamics’. These latent are largely driven same correlated synaptic currents across circuit that determine generation local field potentials (LFPs). Yet, relationship between and LFPs remains unexplored. Here, characterised this for three different regions primate sensorimotor cortex during reaching. correlation was frequency-dependent varied regions. However, any given region, remained stable throughout behaviour: in each primary motor premotor cortices, LFP-latent profile remarkably similar movement planning execution. robust associations neural population help bridge wealth studies reporting correlates behaviour using either type recordings.

Language: Английский

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Neural Decoding for Intracortical Brain–Computer Interfaces

Cyborg and Bionic Systems, Journal Year: 2023, Volume and Issue: 4

Published: Jan. 1, 2023

Brain–computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve quality daily life. To accurately stably effectors, it is important decoders recognize an individual's motor intention from neural activity either noninvasive or intracortical recording. Intracortical recording invasive way measuring electrical with high temporal spatial resolution. Herein, we review recent developments in signal decoding methods brain–computer interfaces. These achieved good performance analyzing controlling robots prostheses nonhuman primates humans. For more complex paradigms rehabilitation other clinical applications, there remains space further improvements decoders.

Language: Английский

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Nonlinear manifolds underlie neural population activity during behaviour

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 19, 2023

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse neuron responses can be well described by relatively few patterns neural co-modulation. The study such low-dimensional structure population has provided important insights into how brain generates Virtually all studies have used linear dimensionality reduction techniques to estimate population-wide co-modulation patterns, constraining them a flat “neural manifold”. Here, we hypothesised that since nonlinear and make thousands distributed recurrent connections likely amplify nonlinearities, manifolds should intrinsically nonlinear. Combining recordings from monkey, mouse, human motor cortex, mouse striatum, show that: 1) are nonlinear; 2) their nonlinearity becomes more evident complex tasks require varied patterns; 3) manifold varies across architecturally distinct regions. Simulations using network models confirmed proposed relationship between circuit connectivity nonlinearity, including differences Thus, underlying generation behaviour inherently nonlinear, properly accounting for nonlinearities will critical as neuroscientists move towards studying numerous regions involved increasingly naturalistic behaviours.

Language: Английский

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Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: May 5, 2023

Abstract Perception of social stimuli (faces and bodies) relies on “holistic” (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving inverted faces/bodies is harder than their upright counterpart. Albeit neuroimaging evidence suggested involvement face-specific brain areas in holistic processing, spatiotemporal dynamics selectivity for still debated. Here, we investigate the processing faces, bodies houses (adopted control non-social category), applying deep learning to high-density electroencephalographic signals (EEG) at source-level. Convolutional neural networks were trained classify cortical EEG responses stimulus orientation (upright/inverted), separately each type (faces, bodies, houses), resulting perform well above chance faces close houses. By explaining network decision, 150–200 ms time interval few visual ventral-stream regions identified mostly relevant discriminating face body (lateral occipital cortex, only, precuneus fusiform lingual gyri), together with two additional dorsal-stream (superior inferior parietal cortices). Overall, proposed approach sensitive detecting activity underlying perceptual phenomena, maximally exploiting discriminant information contained data, may reveal features previously undisclosed, stimulating novel investigations.

Language: Английский

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