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Kim D, Truong PL, Lee CB, Bang H, Choi J, Ham S, Ko JH, Kim K, Lee D, Park HJ. Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402961. [PMID: 38895971 DOI: 10.1002/smll.202402961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/29/2024] [Indexed: 06/21/2024]
Abstract
Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.
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Affiliation(s)
- Dohyung Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Phuoc Loc Truong
- Department of Mechanical Engineering, Gachon University, Gyeonggi, 13120, South Korea
| | - Cheong Beom Lee
- Department of Chemical Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Hyeonsu Bang
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jia Choi
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Seokhyun Ham
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Jong Hwan Ko
- College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Kyeounghak Kim
- Department of Chemical Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Daeho Lee
- Department of Mechanical Engineering, Gachon University, Gyeonggi, 13120, South Korea
| | - Hui Joon Park
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
- Department of Semiconductor Engineering, Hanyang University, Seoul, 04763, South Korea
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2
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Wu Y, Li BZ, Wang L, Fan S, Chen C, Li A, Lin Q, Wang P. An unsupervised real-time spike sorting system based on optimized OSort. J Neural Eng 2023; 20:066015. [PMID: 37972395 DOI: 10.1088/1741-2552/ad0d15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.
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Affiliation(s)
- Yingjiang Wu
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Ben-Zheng Li
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
| | - Liyang Wang
- State Key Laboratory of Analog and Mixed Signal VLSI, University of Macau, Macau, People's Republic of China
- Department of Electrical and Computer Engineering, University of Macau, Macau, People's Republic of China
| | - Shaocan Fan
- School of Electronics and Communication Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen, People's Republic of China
| | - Changhao Chen
- Zhuhai Hokai Medical Instruments Co., Ltd, Zhuhai, People's Republic of China
| | - Anan Li
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, People's Republic of China
| | - Qin Lin
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Panke Wang
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
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Zhang Z, Feng P, Oprea A, Constandinou TG. Calibration-Free and Hardware-Efficient Neural Spike Detection for Brain Machine Interfaces. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:725-740. [PMID: 37216253 DOI: 10.1109/tbcas.2023.3278531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints - the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this article, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18 μm CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86 μW from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.
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Lin C, Han C, Mao J, Yu S, Zhang Z. Multi-channel Wireless Implantable Brain-Computer Interface System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083679 DOI: 10.1109/embc40787.2023.10340603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The implantable brain-computer interface has been widely used in recent years due to its great application potential and research value. Few neural implants have been designed to gather neural spikes, which require a higher sampling frequency than ECoG and LFPs. These systems are still constrained by low channel counts and their bulky size. Furthermore, wire connection is still used in many neural interfaces for further data analysis, facing challenges such as tissue infection, limited movement, and increased noise interference. To address the aforementioned problems, this paper presents a compact multi-channel wireless implantable brain-computer interface system that meets the requirements of spike signals collection and miniaturization. A WiFi module is utilized to transmit information between the system and terminal equipment to eliminate the tethering effects. A 128-channel signal acquisition module, consisting of two pieces of commercial digital electrophysiology amplifier chips, is designed to realize high channel counts for capturing spike events. The proposed system has successfully recorded the analog spike signals from a digital neural signal simulator.
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Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.
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Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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6
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Hanuscheck N, Thalman C, Domingues M, Schmaul S, Muthuraman M, Hetsch F, Ecker M, Endle H, Oshaghi M, Martino G, Kuhlmann T, Bozek K, van Beers T, Bittner S, von Engelhardt J, Vogt J, Vogelaar CF, Zipp F. Interleukin-4 receptor signaling modulates neuronal network activity. J Exp Med 2022; 219:213227. [PMID: 35587822 PMCID: PMC9123307 DOI: 10.1084/jem.20211887] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/13/2021] [Accepted: 04/29/2022] [Indexed: 11/25/2022] Open
Abstract
Evidence is emerging that immune responses not only play a part in the central nervous system (CNS) in diseases but may also be relevant for healthy conditions. We discovered a major role for the interleukin-4 (IL-4)/IL-4 receptor alpha (IL-4Rα) signaling pathway in synaptic processes, as indicated by transcriptome analysis in IL-4Rα–deficient mice and human neurons with/without IL-4 treatment. Moreover, IL-4Rα is expressed presynaptically, and locally available IL-4 regulates synaptic transmission. We found reduced synaptic vesicle pools, altered postsynaptic currents, and a higher excitatory drive in cortical networks of IL-4Rα–deficient neurons. Acute effects of IL-4 treatment on postsynaptic currents in wild-type neurons were mediated via PKCγ signaling release and led to increased inhibitory activity supporting the findings in IL-4Rα–deficient neurons. In fact, the deficiency of IL-4Rα resulted in increased network activity in vivo, accompanied by altered exploration and anxiety-related learning behavior; general learning and memory was unchanged. In conclusion, neuronal IL-4Rα and its presynaptic prevalence appear relevant for maintaining homeostasis of CNS synaptic function.
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Affiliation(s)
- Nicholas Hanuscheck
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Carine Thalman
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Micaela Domingues
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Samantha Schmaul
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Florian Hetsch
- Institute for Pathophysiology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Manuela Ecker
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Heiko Endle
- Department of Molecular and Translational Neuroscience, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases and Center of Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Mohammadsaleh Oshaghi
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gianvito Martino
- Neuroimmunology Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute and Vita Salute San Raffaele University, Milan, Italy
| | - Tanja Kuhlmann
- Institute for Neuropathology, University Hospital Münster, Münster, Germany
| | - Katarzyna Bozek
- Center for Molecular Medicine, Faculty of Medicine and University Hospital Cologne; University of Cologne, Cologne, Germany
| | - Tim van Beers
- Molecular Cell Biology, Institute I of Anatomy, University of Cologne, Cologne, Germany
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jakob von Engelhardt
- Institute for Pathophysiology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Johannes Vogt
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.,Department of Molecular and Translational Neuroscience, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases and Center of Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Christina Francisca Vogelaar
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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7
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Closed-loop sacral neuromodulation for bladder function using dorsal root ganglia sensory feedback in an anesthetized feline model. Med Biol Eng Comput 2022; 60:1527-1540. [PMID: 35349032 PMCID: PMC9124066 DOI: 10.1007/s11517-022-02554-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 03/16/2022] [Indexed: 10/18/2022]
Abstract
Overactive bladder patients suffer from a frequent, uncontrollable urge to urinate, which can lead to a poor quality of life. We aim to improve open-loop sacral neuromodulation therapy by developing a conditional stimulation paradigm using neural recordings from dorsal root ganglia (DRG) as sensory feedback. Experiments were performed in 5 anesthetized felines. We implemented a Kalman filter-based algorithm to estimate the bladder pressure in real-time using sacral-level DRG neural recordings and initiated sacral root electrical stimulation when the algorithm detected an increase in bladder pressure. Closed-loop neuromodulation was performed during continuous cystometry and compared to bladder fills with continuous and no stimulation. Overall, closed-loop stimulation increased bladder capacity by 13.8% over no stimulation (p < 0.001) and reduced stimulation time versus continuous stimulation by 57.7%. High-confidence bladder single units had a reduced sensitivity during stimulation, with lower linear trendline fits and higher pressure thresholds for firing observed during stimulation trials. This study demonstrates the utility of decoding bladder pressure from neural activity for closed-loop control of sacral neuromodulation. An underlying mechanism for sacral neuromodulation may be a reduction in bladder sensory neuron activity during stimulation. Real-time validation during behavioral studies is necessary prior to clinical translation of closed-loop sacral neuromodulation.
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Tringides CM, Mooney DJ. Materials for Implantable Surface Electrode Arrays: Current Status and Future Directions. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107207. [PMID: 34716730 DOI: 10.1002/adma.202107207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Surface electrode arrays are mainly fabricated from rigid or elastic materials, and precisely manipulated ductile metal films, which offer limited stretchability. However, the living tissues to which they are applied are nonlinear viscoelastic materials, which can undergo significant mechanical deformation in dynamic biological environments. Further, the same arrays and compositions are often repurposed for vastly different tissues rather than optimizing the materials and mechanical properties of the implant for the target application. By first characterizing the desired biological environment, and then designing a technology for a particular organ, surface electrode arrays may be more conformable, and offer better interfaces to tissues while causing less damage. Here, the various materials used in each component of a surface electrode array are first reviewed, and then electrically active implants in three specific biological systems, the nervous system, the muscular system, and skin, are described. Finally, the fabrication of next-generation surface arrays that overcome current limitations is discussed.
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Affiliation(s)
- Christina M Tringides
- Harvard Program in Biophysics, Harvard University, Cambridge, MA, 02138, USA
- Harvard-MIT Division in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, 02138, USA
| | - David J Mooney
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, 02138, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
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Shi Y, Ananthakrishnan A, Oh S, Liu X, Hota G, Cauwenberghs G, Kuzum D. A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting. IEEE TRANSACTIONS ON ELECTRON DEVICES 2022; 69:2137-2144. [PMID: 37168652 PMCID: PMC10168101 DOI: 10.1109/ted.2021.3131116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuOx resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuOx memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.
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Affiliation(s)
- Yuhan Shi
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Akshay Ananthakrishnan
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Sangheon Oh
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Xin Liu
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Gopabandhu Hota
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Gert Cauwenberghs
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
| | - Duygu Kuzum
- Electrical and Computer Engineering Department. G. Cauwenberghs is with Bioengineering Department, University of California at San Diego, San Diego, CA 92093, USA
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10
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Zhang Z, Savolainen OW, Constandinou T. Algorithm and hardware considerations for real-time neural signal on-implant processing. J Neural Eng 2022; 19. [PMID: 35130536 DOI: 10.1088/1741-2552/ac5268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/07/2022] [Indexed: 11/12/2022]
Abstract
Objective Various on-workstation neural-spike-based brain machine interface(BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear. Approaches. Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on Microcontroller(MCU) and Field Programmable Gate Array(FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design. Main results. The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3KB RAM and consumes 31.5μW/ch. The FPGA platform only occupies 299 logic cells and 3KB RAM for 128 channels and consumes 0.04μW/ch. Significance. On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.
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Affiliation(s)
- Zheng Zhang
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Oscar W Savolainen
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Timothy Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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11
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Rapeaux AB, Constandinou TG. Implantable brain machine interfaces: first-in-human studies, technology challenges and trends. Curr Opin Biotechnol 2021; 72:102-111. [PMID: 34749248 DOI: 10.1016/j.copbio.2021.10.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/29/2021] [Accepted: 10/02/2021] [Indexed: 11/29/2022]
Abstract
Implantable brain machine interfaces (BMIs) are now on a trajectory to go mainstream, wherein what was once considered last resort will progressively become elective at earlier stages in disease treatment. First-in-human successes have demonstrated the ability to decode highly dexterous motor skills such as handwriting, and speech from human cortical activity. These have been used for cursor and prosthesis control, direct-to-text communication and speech synthesis. Along with these breakthrough studies, technology advancements have enabled the observation of more channels of neural activity through new concepts for centralised/distributed implant architectures. This is complemented by research in flexible substrates, packaging, surgical workflows and data processing. New regulatory guidance and funding has galvanised the field. This culmination of resource, efforts and capability is now attracting significant investment for BMI commercialisation. This paper reviews recent developments and describes the paradigm shift in BMI development that is leading to new innovations, insights and BMI translation.
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Affiliation(s)
- Adrien B Rapeaux
- Department of Electrical and Electronic Engineering, Imperial College London, UK; Centre for Bio-Inspired Technology, Imperial College London, UK; Care Research and Technology (CR&T) based at Imperial College London and the University of Surrey, UK Dementia Research Institute (UK DRI), UK
| | - Timothy G Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, UK; Centre for Bio-Inspired Technology, Imperial College London, UK; Care Research and Technology (CR&T) based at Imperial College London and the University of Surrey, UK Dementia Research Institute (UK DRI), UK.
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Firfilionis D, Hutchings F, Tamadoni R, Walsh D, Turnbull M, Escobedo-Cousin E, Bailey RG, Gausden J, Patel A, Haci D, Liu Y, LeBeau FEN, Trevelyan A, Constandinou TG, O'Neill A, Kaiser M, Degenaar P, Jackson A. A Closed-Loop Optogenetic Platform. Front Neurosci 2021; 15:718311. [PMID: 34566564 PMCID: PMC8462298 DOI: 10.3389/fnins.2021.718311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 08/16/2021] [Indexed: 01/11/2023] Open
Abstract
Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.
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Affiliation(s)
- Dimitrios Firfilionis
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Frances Hutchings
- Digital Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Reza Tamadoni
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Darren Walsh
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Mark Turnbull
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Enrique Escobedo-Cousin
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Richard G. Bailey
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Johannes Gausden
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Aaliyah Patel
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Dorian Haci
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Yan Liu
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Department of Micro-Nano Electronics, Shanghai Jiaotong University, Shanghai, China
| | - Fiona E. N. LeBeau
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew Trevelyan
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Timothy G. Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Anthony O'Neill
- Emerging Technologies and Materials Group, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Patrick Degenaar
- Neuroprosthesis Lab, School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew Jackson
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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13
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Tóth R, Miklós Barth A, Domonkos A, Varga V, Somogyvári Z. Do not waste your electrodes-principles of optimal electrode geometry for spike sorting. J Neural Eng 2021; 18. [PMID: 34181590 DOI: 10.1088/1741-2552/ac0f49] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 06/28/2021] [Indexed: 11/11/2022]
Abstract
Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance.Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampusin vivo.Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance.Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.
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Affiliation(s)
- Róbert Tóth
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Albert Miklós Barth
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Andor Domonkos
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Viktor Varga
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Zoltán Somogyvári
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Neuromicrosystems Ltd, Budapest, Hungary
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Zhang Z, Constandinou TG. Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs. J Neurosci Methods 2021; 354:109103. [PMID: 33617917 DOI: 10.1016/j.jneumeth.2021.109103] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/23/2021] [Accepted: 02/15/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND The progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing. NEW METHODS We have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets. MAIN RESULTS The algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW. COMPARISON WITH EXISTING METHODS The method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources. CONCLUSION The proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future 'high bandwidth' systems' targeting 1000s of channels.
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Affiliation(s)
- Zheng Zhang
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
| | - Timothy G Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; UK Dementia Research Institute (UKDRI) Care Research & Technology Centre, based at Imperial College London and the University of Surrey, UK.
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15
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Nurmikko A. Challenges for Large-Scale Cortical Interfaces. Neuron 2020; 108:259-269. [DOI: 10.1016/j.neuron.2020.10.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/10/2020] [Accepted: 10/12/2020] [Indexed: 12/21/2022]
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16
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Williams I, Brunton E, Rapeaux A, Liu Y, Luan S, Nazarpour K, Constandinou T. SenseBack - An Implantable System for Bidirectional Neural Interfacing. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; PP:1079-1087. [PMID: 32915746 DOI: 10.1109/tbcas.2020.3022839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Chronic in-vivo neurophysiology experiments require highly miniaturized, remotely powered multi-channel neural interfaces which are currently lacking in power or flexibility post implantation. To resolve this problem we present the SenseBack system, a post-implantation reprogrammable wireless 32-channel bidirectional neural interfacing device that can enable chronic peripheral electrophysiology experiments in freely behaving small animals. The large number of channels for a peripheral neural interface, coupled with fully implantable hardware and complete software flexibility enable complex in-vivo studies where the system can adapt to evolving study needs as they arise. In complementary \textit{ex-vivo} and \textit{in-vivo} preparations, we demonstrate that this system can record neural signals and perform high-voltage, bipolar stimulation on any channel. In addition, we demonstrate transcutaneous power delivery and Bluetooth 5 data communication with a PC. The SenseBack system is capable of stimulation on any channel with 20 V of compliance and up to 315 A of current, and highly configurable recording with per-channel adjustable gain and filtering with 8 sets of 10-bit ADCs to sample data at 20 kHz for each channel. To our knowledge this is the first such implantable research platform offering this level of performance and flexibility post-implantation (including complete reprogramming even after encapsulation) for small animal electrophysiology. Here we present initial acute trials, demonstrations and progress towards a system that we expect to enable a wide range of electrophysiology experiments in freely behaving animals.
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Schaffer L, Nagy Z, Kincses Z, Fiath R, Ulbert I. Spatial Information Based OSort for Real-Time Spike Sorting Using FPGA. IEEE Trans Biomed Eng 2020; 68:99-108. [PMID: 32746008 DOI: 10.1109/tbme.2020.2996281] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers. METHODS In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data. The system uses a spatial window-based version of the Online Sorting algorithm, which uses unsupervised template-matching for clustering. RESULTS The test results show that the proposed system can reach an average accuracy of 86% using simulated data (16-32 neurons, 4-10 dB Signal-to-Noise Ratio), while the single-channel clustering version achieves only 74% average accuracy in the same cases on a 128-channel electrode array. The developed system was also tested on in vivo cortical recordings obtained from an anesthetized rat. CONCLUSION The proposed FPGA-based spike sorting system can process more than 11000 spikes/second, so it can be used during in vivo experiments providing real-time feedback on the location and electrophysiological properties of well-separable single units. SIGNIFICANCE The proposed spike sorting system could be used to reduce the positioning error of the closely-packed recording site during a neural measurement.
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Savolainen OW, Constandinou TG. Predicting Single-Unit Activity from Local Field Potentials with LSTMs. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:884-887. [PMID: 33018126 DOI: 10.1109/embc44109.2020.9175265] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) to predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is to determine to what degree the LFP signal can be used as a proxy for SUA, for both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, the results suggest that the prediction quality varies significantly by implant location or animal. However, within each implant location / animal, the prediction quality seems to be correlated with the amount of power in certain LFP frequency bands (0-10, 10-20 and 40-50Hz, standardised LFPs). Secondly, the results suggest that bipolar LFPs are more informative as to SUA than unipolar LFPs. This suggests common mode rejection aids in the elimination of non-local neural information. Thirdly, the best individual bipolar LFPs generally perform better than when using all available unipolar LFPs. This suggests that LFP channel selection may be a simple but effective means of lossy data compression in Wireless Intracortical LFP-based BCIs. Overall, LFPs were moderately predictive of SUA, and improvements can likely be made.
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19
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De Marcellis A, Stanchieri GDP, Faccio M, Palange E, Constandinou TG. A 300 Mbps 37 pJ/bit Pulsed Optical Biotelemetry. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:441-451. [PMID: 32054584 DOI: 10.1109/tbcas.2020.2972733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article reports an implantable transcutaneous telemetry for a brain machine interface that uses a novel optical communication system to achieve a highly energy-efficient link. Based on an pulse-based coding scheme, the system uses sub-nanosecond laser pulses to achieve data rates up to 300 Mbps with relatively low power levels when compared to other methods of wireless communication. This has been implemented using a combination of discrete components (semiconductor laser and driver, fast-response Si photodiode and interface) integrated at board level together with reconfigurable logic (encoder, decoder and processing circuits implemented using Xilinx KCU105 board with Kintex UltraScale FPGA). Experimental validation has been performed using a tissue sample that achieves representative level of attenuation/scattering (porcine skin) in the optical path. Results reveal that the system can operate at data rates up to 300 Mbps with a bit error rate (BER) of less than 10 -10, and an energy efficiency of 37 pJ/bit. This can communicate, for example, 1,024 channels of broadband neural data sampled at 18 kHz, 16-bit with only 11 mW power consumption.
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20
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Memristor networks for real-time neural activity analysis. Nat Commun 2020; 11:2439. [PMID: 32415218 PMCID: PMC7228921 DOI: 10.1038/s41467-020-16261-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 04/17/2020] [Indexed: 12/19/2022] Open
Abstract
The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and store large amounts of raw recording data, followed by extensive processing offline, posing significant challenges to the hardware and preventing real-time analysis and feedback. Here, we demonstrate a memristor-based reservoir computing (RC) system that can potentially analyze neural signals in real-time. We show that the perovskite halide-based memristor can be directly driven by emulated neural spikes, where the memristor state reflects temporal features in the neural spike train. The RC system is successfully used to recognize neural firing patterns, monitor the transition of the firing patterns, and identify neural synchronization states among different neurons. Advanced neuroelectronic systems with such memristor networks can enable efficient neural signal analysis with high spatiotemporal precision, and possibly closed-loop feedback control. Designing energy efficient artificial neural networks for real-time analysis remains a challenge. Here, the authors report the development of a perovskite halide (CsPbI3) memristor-based Reservoir Computing system for real-time recognition of neural firing patterns and neural synchronization states.
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21
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Leopold DA, Park SH. Studying the visual brain in its natural rhythm. Neuroimage 2020; 216:116790. [PMID: 32278093 DOI: 10.1016/j.neuroimage.2020.116790] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 03/26/2020] [Accepted: 03/30/2020] [Indexed: 12/27/2022] Open
Abstract
How the brain fluidly orchestrates visual behavior is a central question in cognitive neuroscience. Researchers studying neural responses in humans and nonhuman primates have mapped out visual response profiles and cognitive modulation in a large number of brain areas, most often using pared down stimuli and highly controlled behavioral paradigms. The historical emphasis on reductionism has placed most studies at one pole of an inherent trade-off between strictly controlled experimental variables and open designs that monitor the brain during its natural modes of operation. This bias toward simplified experiments has strongly shaped the field of visual neuroscience, with little guarantee that the principles and concepts established within that framework will apply more generally. In recent years, a growing number of studies have begun to relax strict experimental control with the aim of understanding how the brain responds under more naturalistic conditions. In this article, we survey research that has explicitly embraced the complexity and rhythm of natural vision. We focus on those studies most pertinent to understanding high-level visual specializations in brains of humans and nonhuman primates. We conclude that representationalist concepts borne from conventional visual experiments fall short in their ability to capture the real-life visual operations undertaken by the brain. More naturalistic approaches, though fraught with experimental and analytic challenges, provide fertile ground for neuroscientists seeking new inroads to investigate how the brain supports core aspects of our daily visual experience.
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Affiliation(s)
- David A Leopold
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Soo Hyun Park
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
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22
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Vizvari Z, Toth A, Sari Z, Klincsik M, Kuljic B, Szakall T, Odry A, Mathe K, Szabo I, Karadi Z, Odry P. Measurement system with real time data converter for conversion of I 2 S data stream to UDP protocol data. Heliyon 2020; 6:e03760. [PMID: 32346631 PMCID: PMC7182730 DOI: 10.1016/j.heliyon.2020.e03760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/01/2020] [Accepted: 04/06/2020] [Indexed: 01/24/2023] Open
Abstract
A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a "low" temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain.
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Affiliation(s)
- Zoltan Vizvari
- Department of Environmental Engineering, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Attila Toth
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Sari
- Department of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Mihaly Klincsik
- Department of Mathematics, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Bojan Kuljic
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Tibor Szakall
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Akos Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
| | - Kalman Mathe
- Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Imre Szabo
- Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Karadi
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Peter Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
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Zhuk M, Zarubin S, Karateev I, Matveyev Y, Gornev E, Krasnikov G, Negrov D, Zenkevich A. On-Chip TaO x -Based Non-volatile Resistive Memory for in vitro Neurointerfaces. Front Neurosci 2020; 14:94. [PMID: 32174805 PMCID: PMC7055297 DOI: 10.3389/fnins.2020.00094] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 01/23/2020] [Indexed: 11/13/2022] Open
Abstract
The development of highly integrated electrophysiological devices working in direct contact with living neuron tissue opens new exciting prospects in the fields of neurophysiology and medicine, but imposes tight requirements on the power dissipated by electronics. On-chip preprocessing of neuronal signals can substantially decrease the power dissipated by external data interfaces, and the addition of embedded non-volatile memory would significantly improve the performance of a co-processor in real-time processing of the incoming information stream from the neuron tissue. Here, we evaluate the parameters of TaO x -based resistive switching (RS) memory devices produced by magnetron sputtering technique and integrated with the 180-nm CMOS field-effect transistors as possible candidates for on-chip memory in the hybrid neurointerface under development. The electrical parameters of the optimized one-transistor-one-resistor (1T-1R) devices, such as the switching voltage (approx. ±1 V), uniformity of the R off/R on ratio (∼10), read/write speed (<40 ns), and the number of the writing cycles (up to 1010), are satisfactory. The energy values for writing and reading out a bit ∼30 and ∼0.1 pJ, respectively, are also suitable for the desired in vitro neurointerfaces, but are still far too high once the prospective in vivo applications are considered. Challenges arising in the course of the prospective fabrication of the proposed TaO x -based RS devices in the back-end-of-line process are identified.
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Affiliation(s)
- Maksim Zhuk
- Laboratory of Functional Materials and Devices for Nanoelectronics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Sergei Zarubin
- Laboratory of Functional Materials and Devices for Nanoelectronics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Igor Karateev
- National Research Center, Kurchatov Institute, Moscow, Russia
| | | | - Evgeny Gornev
- Molecular Electronics Research Institute (MERI), Moscow, Russia
| | | | - Dmitiry Negrov
- Laboratory of Neurocomputing Systems, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Andrei Zenkevich
- Laboratory of Functional Materials and Devices for Nanoelectronics, Moscow Institute of Physics and Technology, Moscow, Russia
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Valencia D, Alimohammad A. A Real-Time Spike Sorting System Using Parallel OSort Clustering. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:1700-1713. [PMID: 31634141 DOI: 10.1109/tbcas.2019.2947618] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article presents an efficient design and implementation of a real-time spike sorting system using unsupervised clustering. We utilize the online sorting (OSort) algorithm and model it first in both floating-point and fixed-point numerical representations to accurately assess the feasibility of our hardware architecture and also reliably analyze the sorting accuracy. For efficient hardware realization of OSort, we propose a modified parallel OSort algorithm. By reducing the number of required memory accesses, the number of computations performed for the management and upkeep of cluster averages and cluster merging is substantially reduced. By limiting the number of supported clusters per channel, the classification/clustering latency is significantly reduced compared to the previously published work, making the designed OSort system applicable for in-vivo spike sorting. The proposed OSort hardware architecture utilizes a novel memory configuration scheme to parallelize the OSort algorithm, which allows us to avoid using relatively large memory queues for storing detected spike waveforms and process them concurrently to the spike cluster management. The characteristics and implementation results of the designed OSort-based spike sorting system on a Xilinx Artix-7 field-programmable gate array (FPGA) are presented. The ASIC implementation of the designed system is estimated to occupy 2.57 mm2 in a standard 32-nm CMOS process. Post-layout power estimation shows that the ASIC will dissipate 2.78 mW, while operating at 24 kHz.
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Putzeys J, Raducanu BC, Carton A, De Ceulaer J, Karsh B, Siegle JH, Van Helleputte N, Harris TD, Dutta B, Musa S, Mora Lopez C. Neuropixels Data-Acquisition System: A Scalable Platform for Parallel Recording of 10 000+ Electrophysiological Signals. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:1635-1644. [PMID: 31545742 DOI: 10.1109/tbcas.2019.2943077] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.
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26
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Mohammadi Z, Kincaid JM, Pun SH, Klug A, Liu C, Lei TC. Computationally inexpensive enhanced growing neural gas algorithm for real-time adaptive neural spike clustering. J Neural Eng 2019; 16:056007. [DOI: 10.1088/1741-2552/ab208c] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Xu W, de Carvalho F, Jackson A. Sequential Neural Activity in Primary Motor Cortex during Sleep. J Neurosci 2019; 39:3698-3712. [PMID: 30842250 PMCID: PMC6510340 DOI: 10.1523/jneurosci.1408-18.2019] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/29/2019] [Accepted: 02/02/2019] [Indexed: 12/17/2022] Open
Abstract
Sequential firing of neurons during sleep is thought to play a role in the consolidation of learning. However, direct evidence for such sequence replay is limited to only a few brain areas and sleep states mainly in rodents. Using a custom-designed wearable neural data logger and chronically implanted electrodes, we made long-term recordings of neural activity in the primary motor cortex of two female nonhuman primates during free behavior and natural sleep. We used the local field potential (LFP) spectrogram to characterize sleep cycles, and examined firing rates, correlations, and sequential firing of neurons at different frequency bands through the cycle. Slow-wave sleep (SWS) was characterized by low neural firing rates and high synchrony, reflecting slow oscillations between cortical down and up states. However, the order in which neurons entered up states was similar to the sequence of neural activity observed at low frequencies during waking behavior. In addition, we found evidence of brief bursts of theta oscillation, associated with non-SWS states, during which neurons fired in strikingly regular sequential order phase-locked to the LFP. Theta sequences were preserved between waking and sleep, but appeared not to resemble the order of neural activity observed at lower frequencies. The sequential firing of neurons during slow oscillations and theta bursts may contribute to the consolidation of procedural memories during sleep.SIGNIFICANCE STATEMENT Replay of sequential neural activity during sleep is believed to support consolidation of daytime learning. Despite a wealth of studies investigating sequential replay in association with episodic and spatial memory, it is unknown whether similar sequences occur in motor areas during sleep. Within long-term neural recordings from monkey motor cortex, we found two distinct patterns of sequential activity during different phases of the natural sleep cycle. Slow-wave sleep was associated with delta-band sequences that resembled low-frequency activity during movement, while occasional brief bursts of theta oscillation were associated with a different order of sequential firing. Our results are the first report of sequential sleep replay in the motor cortex, which may play an important role in consolidation of procedural learning.
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Affiliation(s)
- Wei Xu
- Institute of Neuroscience, Newcastle University, Newcastle NE2 4HH, United Kingdom
| | - Felipe de Carvalho
- Institute of Neuroscience, Newcastle University, Newcastle NE2 4HH, United Kingdom
| | - Andrew Jackson
- Institute of Neuroscience, Newcastle University, Newcastle NE2 4HH, United Kingdom
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Dai J, Zhang P, Sun H, Qiao X, Zhao Y, Ma J, Li S, Zhou J, Wang C. Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface. J Neural Eng 2019; 16:036011. [PMID: 30822756 DOI: 10.1088/1741-2552/ab0bfb] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE For intracortical neurophysiological studies, spike sorting is an important procedure to isolate single units for analyzing specific functions. However, whether spike sorting is necessary or not for neural decoding applications is controversial. Several studies showed that using threshold crossings (TC) instead of spike sorting could also achieve a similar satisfactory performance. However, such studies were limited in similar behavioral tasks, and the neural signal source mainly focused on the motor-related cortical regions. It is not certain if this conclusion is applicable to other situations. Therefore, we compared the performance of TC and spike sorting in neural decoding with more comprehensive paradigms and parameters. APPROACH Two rhesus macaques implanted with Utah or floating microelectrode arrays (FMAs) in motor or sensory-related cortical regions were trained to perform a motor or a sensory task. Data from each monkey were preprocessed with three different schemes: TC, automatic sorting (AS), and manual sorting (MS). A support vector machine was used as the decoder, and the decoding accuracy was used for evaluating the performance of three preprocessing methods. Different neural signal sources, different decoders, and related parameters and decoding stability were further tested to systematically compare three preprocessing methods. MAIN RESULTS TC could achieve a similar (-4.5 RMS threshold) or better (-3.0 RMS threshold) decoding performance compared to the other two sorting methods in the motor or sensory tasks even if the neural signal sources or decoder-related parameters were changed. Moreover, TC was much more stable in neural decoding across sessions and robust to changes of threshold. SIGNIFICANCE Our results indicated that spike-firing patterns could be stably extracted through TC from multiple cortices in both motor and sensory neural decoding applications. Considering the stability of TC, it might be more suitable for neural decoding compared to sorting methods.
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Affiliation(s)
- Jun Dai
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, 27 Taiping Rd, Beijing 100850, People's Republic of China
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