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  1. Article ; Online: Task-driven neural network models predict neural dynamics of proprioception.

    Marin Vargas, Alessandro / Bisi, Axel / Chiappa, Alberto S / Versteeg, Chris / Miller, Lee E / Mathis, Alexander

    Cell

    2024  Volume 187, Issue 7, Page(s) 1745–1761.e19

    Abstract: Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of ... ...

    Abstract Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.
    MeSH term(s) Animals ; Proprioception/physiology ; Neurons/physiology ; Brain/physiology ; Movement/physiology ; Primates ; Neural Networks, Computer
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2024.02.036
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  2. Article ; Online: De novo motor learning creates structure in neural activity that shapes adaptation.

    Chang, Joanna C / Perich, Matthew G / Miller, Lee E / Gallego, Juan A / Clopath, Claudia

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 4084

    Abstract: Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns ... ...

    Abstract Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
    MeSH term(s) Learning/physiology ; Adaptation, Physiological/physiology ; Motor Cortex/physiology ; Animals ; Models, Neurological ; Neural Networks, Computer ; Neurons/physiology ; Movement/physiology ; Nerve Net/physiology
    Language English
    Publishing date 2024-05-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-48008-7
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  3. Article ; Online: Neurocognitive and motor-control challenges for the realization of bionic augmentation.

    Makin, Tamar R / Micera, Silvestro / Miller, Lee E

    Nature biomedical engineering

    2022  Volume 7, Issue 4, Page(s) 344–348

    MeSH term(s) Bionics ; Insulin ; Blood Glucose
    Chemical Substances Insulin ; Blood Glucose
    Language English
    Publishing date 2022-08-31
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2157-846X
    ISSN (online) 2157-846X
    DOI 10.1038/s41551-022-00930-1
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  4. Article ; Online: Short reaction times in response to multi-electrode intracortical microstimulation may provide a basis for rapid movement-related feedback.

    Sombeck, Joseph T / Miller, Lee E

    Journal of neural engineering

    2019  Volume 17, Issue 1, Page(s) 16013

    Abstract: Objective: Tetraplegic patients using brain-machine interfaces can make visually guided reaches with robotic arms. However, restoring proprioceptive feedback to these patients will be critical, as evidenced by the movement deficit in patients with ... ...

    Abstract Objective: Tetraplegic patients using brain-machine interfaces can make visually guided reaches with robotic arms. However, restoring proprioceptive feedback to these patients will be critical, as evidenced by the movement deficit in patients with proprioceptive loss. Proprioception is critical in large part because it provides faster feedback than vision. Intracortical microstimulation (ICMS) is a promising approach, but the ICMS-evoked reaction time (RT) is typically slower than that to natural proprioceptive and often even visual cues, implying that ICMS feedback may not be fast enough to guide movement.
    Approach: For most sensory modalities, RT decreases with increased stimulus intensity. Thus, it may be that stimulation intensities beyond what has previously been used will result in faster RTs. To test this, we compared the RT to ICMS applied through multi-electrode arrays in area 2 of somatosensory cortex to that of mechanical and visual cues.
    Main results: We found that the RT to single-electrode ICMS decreased with increased current, frequency, and train length. For 100 µA, 330 Hz stimulation, the highest single-electrode intensity we tested routinely, most electrodes resulted in RTs slower than the mechanical cue but slightly faster than the visual cue. While increasing the current beyond 100 µA resulted in faster RTs, sustained stimulation at this level may damage tissue. Alternatively, by stimulating through multiple electrodes (mICMS), a large amount of current can be injected while keeping that through each electrode at a safe level. We found that stimulation with at least 480 µA equally distributed over 16 electrodes could produce RTs as much as 20 ms faster than the mechanical cue, roughly the conduction delay to cortex from the periphery.
    Significance: These results suggest that mICMS may provide a means to supply rapid, movement-related feedback. Future neuroprosthetics may need spatiotemporally patterned mICMS to convey useful somatosensory information. Novelty & Significance Intracortical microstimulation (ICMS) is a promising approach for providing artificial somatosensation to patients with spinal cord injury or limb amputation, but in prior experiments, subjects have been unable to respond as quickly to it as to natural cues. We have investigated the use of multi-electrode stimulation (mICMS) and discovered that it can produce reaction times as fast or faster even than natural mechanical cues. Although our stimulus trains were not modulated in time, this result opens the door to more complex spatiotemporal patterns of mICMS that might be used to rapidly write in complex somatosensory information to the CNS.
    MeSH term(s) Animals ; Brain-Computer Interfaces ; Electric Stimulation/methods ; Electrodes, Implanted ; Feedback, Sensory/physiology ; Macaca mulatta ; Male ; Microelectrodes ; Photic Stimulation/methods ; Reaction Time/physiology ; Somatosensory Cortex/physiology
    Language English
    Publishing date 2019-12-17
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2170901-4
    ISSN 1741-2552 ; 1741-2560
    ISSN (online) 1741-2552
    ISSN 1741-2560
    DOI 10.1088/1741-2552/ab5cf3
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  5. Article ; Online: Cuneate nucleus: The somatosensory gateway to the brain.

    Versteeg, Christopher / Chowdhury, Raeed H / Miller, Lee E

    Current opinion in physiology

    2021  Volume 20, Page(s) 206–215

    Abstract: Much remains unknown about the transformation of proprioceptive afferent input from the periphery to the cortex. Until recently, the only recordings from neurons in the cuneate nucleus (CN) were from anesthetized animals. We are beginning to learn more ... ...

    Abstract Much remains unknown about the transformation of proprioceptive afferent input from the periphery to the cortex. Until recently, the only recordings from neurons in the cuneate nucleus (CN) were from anesthetized animals. We are beginning to learn more about how the sense of proprioception is transformed as it propagates centrally. Recent recordings from microelectrode arrays chronically implanted in CN have revealed that CN neurons with muscle-like properties have a greater sensitivity to active reaching movements than to passive limb displacement, and we find that these neurons have receptive fields that resemble single muscles. In this review, we focus on the varied uses of proprioceptive input and the possible role of CN in processing this information.
    Language English
    Publishing date 2021-02-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 2918626-2
    ISSN 2468-8673 ; 2468-8681
    ISSN (online) 2468-8673
    ISSN 2468-8681
    DOI 10.1016/j.cophys.2021.02.004
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  6. Article: Nonlinear manifolds underlie neural population activity during behaviour.

    Fortunato, Cátia / Bennasar-Vázquez, Jorge / Park, Junchol / Chang, Joanna C / Miller, Lee E / Dudman, Joshua T / Perich, Matthew G / Gallego, Juan A

    bioRxiv : the preprint server for biology

    2024  

    Abstract: There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural ... ...

    Abstract There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
    Language English
    Publishing date 2024-04-25
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.07.18.549575
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  7. Article: De novo

    Chang, Joanna C / Perich, Matthew G / Miller, Lee E / Gallego, Juan A / Clopath, Claudia

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes ... ...

    Abstract Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during
    Language English
    Publishing date 2023-05-24
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.05.23.541925
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  8. Article ; Online: Using adversarial networks to extend brain computer interface decoding accuracy over time.

    Ma, Xuan / Rizzoglio, Fabio / Bodkin, Kevin L / Perreault, Eric / Miller, Lee E / Kennedy, Ann

    eLife

    2023  Volume 12

    Abstract: Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time ... ...

    Abstract Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called 'Adversarial Domain Adaptation Network' (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by Factor Analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.
    MeSH term(s) Animals ; Brain-Computer Interfaces ; Acclimatization ; Brain ; Coleoptera ; Heart
    Language English
    Publishing date 2023-08-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.84296
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  9. Article ; Online: From unstable input to robust output.

    Wimalasena, Lahiru N / Miller, Lee E / Pandarinath, Chethan

    Nature biomedical engineering

    2020  Volume 4, Issue 7, Page(s) 665–667

    MeSH term(s) Brain-Computer Interfaces ; Models, Biological
    Language English
    Publishing date 2020-07-13
    Publishing country England
    Document type Journal Article ; Comment
    ISSN 2157-846X
    ISSN (online) 2157-846X
    DOI 10.1038/s41551-020-0587-9
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  10. Article ; Online: Area 2 of primary somatosensory cortex encodes kinematics of the whole arm.

    Chowdhury, Raeed H / Glaser, Joshua I / Miller, Lee E

    eLife

    2020  Volume 9

    Abstract: Proprioception, the sense of body position, movement, and associated forces, remains poorly understood, despite its critical role in movement. Most studies of area 2, a proprioceptive area of somatosensory cortex, have simply compared neurons' activities ...

    Abstract Proprioception, the sense of body position, movement, and associated forces, remains poorly understood, despite its critical role in movement. Most studies of area 2, a proprioceptive area of somatosensory cortex, have simply compared neurons' activities to the movement of the hand through space. Using motion tracking, we sought to elaborate this relationship by characterizing how area 2 activity relates to whole arm movements. We found that a whole-arm model, unlike classic models, successfully predicted how features of neural activity changed as monkeys reached to targets in two workspaces. However, when we then evaluated this whole-arm model across active and passive movements, we found that many neurons did not consistently represent the whole arm over both conditions. These results suggest that 1) neural activity in area 2 includes representation of the whole arm during reaching and 2) many of these neurons represented limb state differently during active and passive movements.
    MeSH term(s) Animals ; Biomechanical Phenomena/physiology ; Hand/physiology ; Macaca mulatta ; Movement/physiology ; Neurons/physiology ; Proprioception/physiology ; Somatosensory Cortex/physiology ; Task Performance and Analysis ; Upper Extremity/physiology
    Language English
    Publishing date 2020-01-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.48198
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