Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback

Abbasi, Aamir; Estebanez, Luc; Goueytes, Dorian; Lassagne, Henri; Shulz, Daniel E.; Ego-Stengel, Valérie

doi:10.1101/2019.12.12.873794

Cited by 3 publications

(6 citation statements)

References 70 publications

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“…Previous work has focused on designing BMI feedback that closely matches the spatiotemporal cortical dynamics triggered by peripheral stimulations (Abbasi et al, 2018;Flesher et al, 2021Flesher et al, , 2016O'Connor et al, 2013). In line with this strategy, we recently demonstrated that spatially distributed feedback that follows the underlying topographical structure of the cortex better supports learning and control of a brain-machine interface (Abbasi et al, 2019).…”

Section: Discussionmentioning

confidence: 66%

“…We therefore designed a distributed feedback pattern that obeys the integrative rules of spatial and temporal contiguity that we uncovered (Abbasi et al, 2019), but that introduced a novel, circular topographical representation of the angular position of a robotic joint.…”

Section: Discussionmentioning

confidence: 99%

“…All neuronal signals were sampled at 30 kHz and the records were stored. A detailed description of the quality of the signals and of their stability across sessions is available in (Abbasi et al 2019), where the very same methods were used. Given the duration of the experiments described here, we estimated that a single neuron picked arbitrarily at the start of the training may not be recorded stably across the whole training period.…”

Section: Neuronal Electrophysiological Recordingsmentioning

confidence: 99%

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Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback

Goueytes¹,

Lassagne²,

Shulz³

et al. 2022

J. Neural Eng.

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Objective Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface. Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop brain-machine interface. Approach We tested the ability of the mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint. Main results We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control. Significance These findings suggest that in the context of a closed-loop brain-machine interface, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.

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Section: Discussionmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Neuronal Electrophysiological Recordingsmentioning

confidence: 99%

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Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback

Goueytes¹,

Lassagne²,

Shulz³

et al. 2022

J. Neural Eng.

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“…Two articles each were devoted to sensory processing disorders (Zhang et al, 2021;Sun et al, 2022) and vision related disease (Neely et al, 2018;Scheyltjens et al, 2018), respectively. One article considered the motor cortex, and the results showed that mice can identify neuronal activity caused by photostimuli (Abbasi et al, 2018). In the context of psychiatric disease, one article demonstrated the ability of animals to utilize an artificial cerebral channel in a behaviorally significant manner (Prsa et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Belkacem

Jamil

Khalid

et al. 2023

Front. Hum. Neurosci.

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Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

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“…Other studies on adaptation to force-field perturbations and visumomotor rotations, where learning occurs on similarly short timescales, have provided evidence for changes upstream of M1 [38][39][40][41][42], such as changes in preparatory activity [43,44] and dependence on sensory processing [38,[45][46][47]. The presence and structure of sensory feedback is also critical for successful BCI learning [31][32][33][48][49][50]. This motivated us to explore the interaction between input plasticity and preserved recurrent connectivity in generating adapted trajectories during BCI learning.…”

Section: Introductionmentioning

confidence: 99%

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Gurnani,

Liu,

Brunton

2024

Preprint

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Latent dynamical models of the primary motor cortex (M1) have revealed fundamental neural computations underlying motor control; however, such models often overlook the impact of sensory feedback, which can continually update cortical dynamics and correct for external perturbations. This suggests a critical need to model the interaction between sensory feedback and intrinsic dynamics. Such models would also benefit the design of brain-computer interfaces (BCIs) that decode neural activity in real time, where both user learning and proficient control require feedback. Here we investigate the flexible feedback modulation of cortical dynamics and demonstrate its impact on BCI task performance and short-term learning. By training recurrent network models with real-time sensory feedback on a simple 2D reaching task, analogous to BCI cursor control, we show how previously reported M1 activity patterns can be reinterpreted as arising from feedback-driven dynamics. Next, by incorporating adaptive controllers upstream of M1, we make a testable prediction that short-term learning for a new BCI decoder is facilitated by plasticity of inputs to M1, including remapping of sensory feedback, beyond the plasticity of recurrent connections within M1. This input-driven dynamical structure also determines the speed of adaptation and learning outcomes, and explains a continuous form of learning variability. Thus, our work highlights the need to model input-dependent latent dynamics for motor control and clarifies how constraints on learning arise from both the statistical characteristics and the underlying dynamical structure of neural activity.

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Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback

Abstract: Topographic representations of the peripheral sensory organs are a prominent feature of primary sensory areas in 8

Cited by 3 publications

References 70 publications

Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback

Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

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