1
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Zhao Z, Yu H, Wisniewski DJ, Cea C, Ma L, Trautmann EM, Churchland MM, Gelinas JN, Khodagholy D. Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite. Adv Sci (Weinh) 2024:e2308014. [PMID: 38600655 DOI: 10.1002/advs.202308014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/07/2024] [Indexed: 04/12/2024]
Abstract
Epidermal electrophysiology is a non-invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode-to-electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic-conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays. High-density, conformable electrodes are also microfabricated that adhere to the ACI and follow the curvilinear surface of the skin. The results show that ACI significantly enhances the spatial resolution of epidermal electromyography (EMG) recording compared to conductive paste, permitting the acquisition of single muscle action potentials with distinct spatial profiles. The high-density EMG in developing mice, non-human primates, and humans is validated. Overall, high spatial-resolution epidermal electrophysiology enabled by ACI has the potential to advance clinical diagnostics of motor system disorders and enhance data quality for human-computer interface applications.
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Affiliation(s)
- Zifang Zhao
- Department of Electrical Engineering, Columbia University, New York, 10027, USA
| | - Han Yu
- Department of Electrical Engineering, Columbia University, New York, 10027, USA
| | - Duncan J Wisniewski
- Department of Electrical Engineering, Columbia University, New York, 10027, USA
| | - Claudia Cea
- Department of Electrical Engineering, Columbia University, New York, 10027, USA
| | - Liang Ma
- Department of Biomedical Engineering, Columbia University, New York, 10027, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University, New York, NY, 10032, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, 10027, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, 10032, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, 10027, USA
- Kavli Institute for Brain Science, Columbia University, New York, 10032, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, USA
| | - Jennifer N Gelinas
- Department of Biomedical Engineering, Columbia University, New York, 10027, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, 10032, USA
| | - Dion Khodagholy
- Department of Electrical Engineering, Columbia University, New York, 10027, USA
- Department of Electrical Engineering, University of California, Irvine, CA, 92697, USA
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2
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Coughlin B, Muñoz W, Kfir Y, Young MJ, Meszéna D, Jamali M, Caprara I, Hardstone R, Khanna A, Mustroph ML, Trautmann EM, Windolf C, Varol E, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Mark Richardson R, Williams ZM, Cash SS, Paulk AC. Modified Neuropixels probes for recording human neurophysiology in the operating room. Nat Protoc 2023; 18:2927-2953. [PMID: 37697108 DOI: 10.1038/s41596-023-00871-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/08/2023] [Indexed: 09/13/2023]
Abstract
Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease. Here, we provide a better understanding of the capabilities and challenges of using Neuropixels as a research tool to study human neurophysiology, with the hope that this information may inform future efforts toward regulatory approval of Neuropixels probes as research devices. In perioperative procedures, the major concerns are the initial sterility of the device, maintaining a sterile field during surgery, having multiple referencing and grounding schemes available to de-noise recordings (if necessary), protecting the silicon probe from accidental contact before insertion and obtaining high-quality action potential and local field potential recordings. The research team ensures that the device is fully operational while coordinating with the surgical team to remove sources of electrical noise that could otherwise substantially affect the signals recorded by the sensitive hardware. Prior preparation using the equipment and training in human clinical research and working in operating rooms maximize effective communication within and between the teams, ensuring high recording quality and minimizing the time added to the surgery. The perioperative procedure requires ~4 h, and the entire protocol requires multiple weeks.
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Affiliation(s)
- Brian Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Michael J Young
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Richard Hardstone
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Arjun Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, USA
| | - Charlie Windolf
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Erdem Varol
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Computer Science and Engineering, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sergey D Stavisky
- Department of Neurological Surgery, University of California Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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3
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Stine GM, Trautmann EM, Jeurissen D, Shadlen MN. A neural mechanism for terminating decisions. Neuron 2023; 111:2601-2613.e5. [PMID: 37352857 PMCID: PMC10565788 DOI: 10.1016/j.neuron.2023.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/25/2023]
Abstract
The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are established in association cortex, but the site and mechanism of termination are unknown. Here, we show that the superior colliculus (SC) plays a causal role in terminating decisions, and we provide evidence for a mechanism by which this occurs. We recorded simultaneously from neurons in the lateral intraparietal area (LIP) and SC while monkeys made perceptual decisions. Despite similar trial-averaged activity, we found distinct single-trial dynamics in the two areas: LIP displayed drift-diffusion dynamics and SC displayed bursting dynamics. We hypothesized that the bursts manifest a threshold mechanism applied to signals represented in LIP to terminate the decision. Consistent with this hypothesis, SC inactivation produced behavioral effects diagnostic of an impaired threshold sensor and prolonged the buildup of activity in LIP. The results reveal the transformation from deliberation to commitment.
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Affiliation(s)
- Gabriel M Stine
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Danique Jeurissen
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Michael N Shadlen
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
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4
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Williams AH, Poole B, Maheswaranathan N, Dhawale AK, Fisher T, Wilson CD, Brann DH, Trautmann EM, Ryu S, Shusterman R, Rinberg D, Ölveczky BP, Shenoy KV, Gangul S. Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping. Neuron 2023; 111:1685. [PMID: 37201504 DOI: 10.1016/j.neuron.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
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5
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Trautmann EM, Hesse JK, Stine GM, Xia R, Zhu S, O'Shea DJ, Karsh B, Colonell J, Lanfranchi FF, Vyas S, Zimnik A, Steinmann NA, Wagenaar DA, Andrei A, Lopez CM, O'Callaghan J, Putzeys J, Raducanu BC, Welkenhuysen M, Churchland M, Moore T, Shadlen M, Shenoy K, Tsao D, Dutta B, Harris T. Large-scale high-density brain-wide neural recording in nonhuman primates. bioRxiv 2023:2023.02.01.526664. [PMID: 37205406 PMCID: PMC10187172 DOI: 10.1101/2023.02.01.526664] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmatically select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.
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6
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Marshall NJ, Glaser JI, Trautmann EM, Amematsro EA, Perkins SM, Shadlen MN, Abbott LF, Cunningham JP, Churchland MM. Flexible neural control of motor units. Nat Neurosci 2022; 25:1492-1504. [PMID: 36216998 PMCID: PMC9633430 DOI: 10.1038/s41593-022-01165-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/12/2022] [Indexed: 01/13/2023]
Abstract
Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Henneman's size principle. Although this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. In this study, we developed an isometric task that allowed stable MU recordings, in a rhesus macaque, even during rapidly changing forces. Patterns of MU activity were surprisingly behavior-dependent and could be accurately described only by assuming multiple drives. Consistent with flexible descending control, microstimulation of neighboring cortical sites recruited different MUs. Furthermore, the cortical population response displayed sufficient degrees of freedom to potentially exert fine-grained control. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability.
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Affiliation(s)
- Najja J Marshall
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Joshua I Glaser
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
| | - Elom A Amematsro
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Sean M Perkins
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Michael N Shadlen
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
- Howard Hughes Medical Institute, Columbia University, New York, NY, USA
| | - L F Abbott
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
- Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA.
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7
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Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
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Affiliation(s)
- Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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8
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Sun X, O'Shea DJ, Golub MD, Trautmann EM, Vyas S, Ryu SI, Shenoy KV. Cortical preparatory activity indexes learned motor memories. Nature 2022; 602:274-279. [PMID: 35082444 PMCID: PMC9851374 DOI: 10.1038/s41586-021-04329-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 12/09/2021] [Indexed: 01/21/2023]
Abstract
The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task1 in a curl force field that elicited new muscle forces for some, but not all, movement directions2,3. We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated4 existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field5,6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space7-9. The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire.
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Affiliation(s)
- Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Matthew D Golub
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Eric M Trautmann
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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9
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Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire MP, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K, Shenoy KV. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nat Commun 2021; 12:3689. [PMID: 34140486 PMCID: PMC8211867 DOI: 10.1038/s41467-021-23884-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/19/2021] [Indexed: 02/05/2023] Open
Abstract
Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - James H Marshel
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ailey Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sam Vesuna
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Lucas Cofer
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Gergő Bohner
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Will Allen
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Isaac Kauvar
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sean Quirin
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Matthew P Whitmire
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | | | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Karl Deisseroth
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
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10
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Williams AH, Poole B, Maheswaranathan N, Dhawale AK, Fisher T, Wilson CD, Brann DH, Trautmann EM, Ryu S, Shusterman R, Rinberg D, Ölveczky BP, Shenoy KV, Ganguli S. Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping. Neuron 2019; 105:246-259.e8. [PMID: 31786013 DOI: 10.1016/j.neuron.2019.10.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/17/2019] [Accepted: 10/10/2019] [Indexed: 12/22/2022]
Abstract
Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.
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Affiliation(s)
- Alex H Williams
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA.
| | - Ben Poole
- Google Brain, Google Inc., Mountain View, CA 94043, USA
| | | | - Ashesh K Dhawale
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Tucker Fisher
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA
| | - Christopher D Wilson
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA
| | - David H Brann
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Eric M Trautmann
- Neuroscience Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Stephen Ryu
- Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Roman Shusterman
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Dmitry Rinberg
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Krishna V Shenoy
- Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Surya Ganguli
- Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Google Brain, Google Inc., Mountain View, CA 94043, USA.
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11
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Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O'Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, Shenoy KV. Accurate Estimation of Neural Population Dynamics without Spike Sorting. Neuron 2019; 103:292-308.e4. [PMID: 31171448 DOI: 10.1016/j.neuron.2019.05.003] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/06/2019] [Accepted: 04/30/2019] [Indexed: 11/25/2022]
Abstract
A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
| | - Sergey D Stavisky
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Subhaneil Lahiri
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Katherine C Ames
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Neuroscience, Columbia University, New York, NY, USA
| | - Matthew T Kaufman
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Palo Alto Medical Foundation, Palo Alto, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Neurosciences Program, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Neurobiology, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford, CA, USA; Bio-X Program, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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12
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Pandarinath C, O'Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods 2018; 15:805-815. [PMID: 30224673 PMCID: PMC6380887 DOI: 10.1038/s41592-018-0109-9] [Citation(s) in RCA: 242] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 06/28/2018] [Indexed: 01/28/2023]
Abstract
Neuroscience is experiencing a revolution in which simultaneous recording
of many thousands of neurons is revealing population dynamics that are not
apparent from single-neuron responses. This structure is typically extracted
from trial-averaged data, but deeper understanding requires studying
single-trial phenomena, which is challenging due to incomplete sampling of the
neural population, trial-to-trial variability, and fluctuations in action
potential timing. We introduce Latent Factor Analysis via Dynamical Systems
(LFADS), a deep learning method to infer latent dynamics from single-trial
neural spiking data. LFADS uses a nonlinear dynamical system to infer the
dynamics underlying observed spiking activity and to extract
‘de-noised’ single-trial firing rates. When applied to a variety
of monkey and human motor cortical datasets, LFADS predicts observed behavioral
variables with unprecedented accuracy, extracts precise estimates of neural
dynamics on single trials, infers perturbations to those dynamics that correlate
with behavioral choices, and combines data from non-overlapping recording
sessions spanning months to improve inference of underlying dynamics.
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Affiliation(s)
- Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA. .,Department of Neurosurgery, Emory University, Atlanta, GA, USA. .,Department of Neurosurgery, Stanford University, Stanford, CA, USA. .,Department of Electrical Engineering, Stanford University, Stanford, CA, USA. .,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Jasmine Collins
- Google AI, Google Inc., Mountain View, CA, USA.,University of California, Berkeley, Berkeley, CA, USA
| | - Rafal Jozefowicz
- Google AI, Google Inc., Mountain View, CA, USA.,OpenAI, San Francisco, CA, USA
| | - Sergey D Stavisky
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.,Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Jonathan C Kao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Matthew T Kaufman
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Leigh R Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Veterans Affairs Medical Center, Providence, RI, USA.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.,Department of Neurobiology, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Bio-X Program, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - L F Abbott
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.,Department of Neuroscience, Columbia University, New York, NY, USA.,Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA. .,Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA. .,Google AI, Google Inc., Mountain View, CA, USA.
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