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Shokri M, Gogliettino AR, Hottowy P, Sher A, Litke AM, Chichilnisky EJ, Pequito S, Muratore D. Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach. J Neural Eng 2024; 21:016022. [PMID: 38271715 PMCID: PMC10853761 DOI: 10.1088/1741-2552/ad228f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/08/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R2=0.951for human 1 andR2=0.944for human 2).Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.
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Affiliation(s)
- Mohammad Shokri
- Delft Center for Systems and Control, Delft University of Technology, Delft 2628 CN, The Netherlands
| | - Alex R Gogliettino
- Neurosciences PhD Program, Stanford University, Stanford, CA 94305, United States of America
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, United States of America
| | - Paweł Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Krakow, Krakow, Poland
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - E J Chichilnisky
- Departments of Neurosurgery and Ophthalmology, Stanford University, Stanford, CA 94305, United States of America
| | - Sérgio Pequito
- Division of Systems and Control, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Dante Muratore
- Microelectronics Department, Delft University of Technology, Delft 2628 CN, The Netherlands
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Haggie L, Schmid L, Röhrle O, Besier T, McMorland A, Saini H. Linking cortex and contraction-Integrating models along the corticomuscular pathway. Front Physiol 2023; 14:1095260. [PMID: 37234419 PMCID: PMC10206006 DOI: 10.3389/fphys.2023.1095260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.
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Affiliation(s)
- Lysea Haggie
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Schmid
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
| | - Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Angus McMorland
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Exercise Sciences, University of Auckland, Auckland, New Zealand
| | - Harnoor Saini
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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3
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Alavi SMM, Vila-Rodriguez F, Mahdi A, Goetz SM. Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation. Biomed Eng Lett 2023; 13:119-127. [PMID: 37124104 PMCID: PMC10130260 DOI: 10.1007/s13534-022-00259-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/26/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023] Open
Abstract
This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.
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Affiliation(s)
- S. M. Mahdi Alavi
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Fidel Vila-Rodriguez
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Adam Mahdi
- Surrey Institute for People-Centred AI, University of Surrey, Surrey, UK
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Stefan M. Goetz
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC USA
- Department of Neurosurgery, Duke University, Durham, NC USA
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Magliaro C, Ahluwalia A. Biomedical Research on Substances of Abuse: The Italian Case Study. Altern Lab Anim 2022; 50:423-436. [PMID: 36222242 DOI: 10.1177/02611929221132215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Substances of abuse have the potential to cause addiction, habituation or altered consciousness. Most of the research on these substances focuses on addiction, and is carried out through observational and clinical studies on humans, or experimental studies on animals. The transposition of the EU Directive 2010/63 into Italian law in 2014 (IT Law 2014/26) includes a ban on the use of animals for research on substances of abuse. Since then, in Italy, public debate has continued on the topic, while the application of the Article prohibiting animal research in this area has been postponed every couple of years. In the light of this debate, we briefly review a range of methodologies - including animal and non-animal, as well as patient or population-based studies - that have been employed to address the biochemical, neurobiological, toxicological, clinical and behavioural effects of substances of abuse and their dependency. We then discuss the implications of the Italian ban on the use of animals for such research, proposing concrete and evidence-based solutions to allow scientists to pursue high-quality basic and translational studies within the boundaries of the regulatory and legislative framework.
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Affiliation(s)
- Chiara Magliaro
- Research Centre 'E. Piaggio', 9310University of Pisa, Pisa, Italy.,Department of Information Engineering, 9310University of Pisa, Pisa, Italy.,Interuniversity Centre for the Promotion of 3R Principles in Teaching and Research (Centro 3R), Pisa, Italy
| | - Arti Ahluwalia
- Research Centre 'E. Piaggio', 9310University of Pisa, Pisa, Italy.,Department of Information Engineering, 9310University of Pisa, Pisa, Italy.,Interuniversity Centre for the Promotion of 3R Principles in Teaching and Research (Centro 3R), Pisa, Italy
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5
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Xiao P, Liu X. Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference. J Neural Eng 2022; 19. [PMID: 35947962 DOI: 10.1088/1741-2552/ac88a0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment. APPROACH To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the Variational Bayesian Inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters. MAIN RESULTS Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved. SIGNIFICANCE Our work can be applied to rapidly decode large-scale multichannel neural spike trains in brain-machine interfaces.
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Affiliation(s)
- Ping Xiao
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, Jiangsu, 210016, CHINA
| | - Xinsheng Liu
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, Jiangsu, 210016, CHINA
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Samejima S, Henderson R, Pradarelli J, Mondello SE, Moritz CT. Activity-dependent plasticity and spinal cord stimulation for motor recovery following spinal cord injury. Exp Neurol 2022; 357:114178. [PMID: 35878817 DOI: 10.1016/j.expneurol.2022.114178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/22/2022] [Accepted: 07/16/2022] [Indexed: 02/07/2023]
Abstract
Spinal cord injuries lead to permanent physical impairment despite most often being anatomically incomplete disruptions of the spinal cord. Remaining connections between the brain and spinal cord create the potential for inducing neural plasticity to improve sensorimotor function, even many years after injury. This narrative review provides an overview of the current evidence for spontaneous motor recovery, activity-dependent plasticity, and interventions for restoring motor control to residual brain and spinal cord networks via spinal cord stimulation. In addition to open-loop spinal cord stimulation to promote long-term neuroplasticity, we also review a more targeted approach: closed-loop stimulation. Lastly, we review mechanisms of spinal cord neuromodulation to promote sensorimotor recovery, with the goal of advancing the field of rehabilitation for physical impairments following spinal cord injury.
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Affiliation(s)
- Soshi Samejima
- International Collaboration on Repair Discoveries, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Medicine, Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Richard Henderson
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Jared Pradarelli
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Sarah E Mondello
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Chet T Moritz
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; Center for Neurotechnology, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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7
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Lin Q, Zhang Y, Zhang Y, Zhuang W, Zhao B, Ke X, Peng T, You T, Jiang Y, Yilifate A, Huang W, Hou L, You Y, Huai Y, Qiu Y, Zheng Y, Ou H. The Frequency Effect of the Motor Imagery Brain Computer Interface Training on Cortical Response in Healthy Subjects: A Randomized Clinical Trial of Functional Near-Infrared Spectroscopy Study. Front Neurosci 2022; 16:810553. [PMID: 35431792 PMCID: PMC9008330 DOI: 10.3389/fnins.2022.810553] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/07/2022] [Indexed: 11/18/2022] Open
Abstract
Background The motor imagery brain computer interface (MI-BCI) is now available in a commercial product for clinical rehabilitation. However, MI-BCI is still a relatively new technology for commercial rehabilitation application and there is limited prior work on the frequency effect. The MI-BCI has become a commercial product for clinical neurological rehabilitation, such as rehabilitation for upper limb motor dysfunction after stroke. However, the formulation of clinical rehabilitation programs for MI-BCI is lack of scientific and standardized guidance, especially limited prior work on the frequency effect. Therefore, this study aims at clarifying how frequency effects on MI-BCI training for the plasticity of the central nervous system. Methods Sixteen young healthy subjects (aged 22.94 ± 3.86 years) were enrolled in this randomized clinical trial study. Subjects were randomly assigned to a high frequency group (HF group) and low frequency group (LF group). The HF group performed MI-BCI training once per day while the LF group performed once every other day. All subjects performed 10 sessions of MI-BCI training. functional near-infrared spectroscopy (fNIRS) measurement, Wolf Motor Function Test (WMFT) and brain computer interface (BCI) performance were assessed at baseline, mid-assessment (after completion of five BCI training sessions), and post-assessment (after completion of 10 BCI training sessions). Results The results from the two-way ANOVA of beta values indicated that GROUP, TIME, and GROUP × TIME interaction of the right primary sensorimotor cortex had significant main effects [GROUP: F(1,14) = 7.251, P = 0.010; TIME: F(2,13) = 3.317, P = 0.046; GROUP × TIME: F(2,13) = 5.676, P = 0.007]. The degree of activation was affected by training frequency, evaluation time point and interaction. The activation of left primary sensory motor cortex was also affected by group (frequency) (P = 0.003). Moreover, the TIME variable was only significantly different in the HF group, in which the beta value of the mid-assessment was higher than that of both the baseline assessment (P = 0.027) and post-assessment (P = 0.001), respectively. Nevertheless, there was no significant difference in the results of WMFT between HF group and LF group. Conclusion The major results showed that more cortical activation and better BCI performance were found in the HF group relative to the LF group. Moreover, the within-group results also showed more cortical activation after five sessions of BCI training and better BCI performance after 10 sessions in the HF group, but no similar effects were found in the LF group. This pilot study provided an essential reference for the formulation of clinical programs for MI-BCI training in improvement for upper limb dysfunction.
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Affiliation(s)
- Qiang Lin
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
- Department of Rehabilitation, Guangzhou Key Laboratory of Enhanced Recovery After Abdominal Surgery, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yanni Zhang
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Yajie Zhang
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Wanqi Zhuang
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Biyi Zhao
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Xiaomin Ke
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Tingting Peng
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Tingting You
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Yongchun Jiang
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Anniwaer Yilifate
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Wei Huang
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Lingying Hou
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yaoyao You
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yaping Huai
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Yaxian Qiu
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
- Yaxian Qiu,
| | - Yuxin Zheng
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
- Yuxin Zheng,
| | - Haining Ou
- Department of Rehabilitation, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Fifth Clinical School, Guangzhou Medical University, Guangzhou, China
- Department of Rehabilitation, Guangzhou Key Laboratory of Enhanced Recovery After Abdominal Surgery, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Haining Ou,
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8
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Sosa R, Alcalá E. The nervous system as a solution for implementing closed negative feedback control loops. J Exp Anal Behav 2022; 117:279-300. [PMID: 35119112 DOI: 10.1002/jeab.736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 01/15/2023]
Abstract
Behavior can be regarded as the output of a system (action), as a function linking stimulus to response (reaction), or as an abstraction of the bidirectional relationship between the environment and the organism (interaction). When considering the latter possibility, a relevant question arises concerning how an organism can materially and continuously implement such a relationship during its lifetime in order to perpetuate itself. The feedback control approach has taken up the task of answering just that question. During the last several decades, said approach has been progressing and has started to be recognized as a paradigm shift, superseding certain canonical notions in mainstream behavior analysis, cognitive psychology, and even neuroscience. In this paper, we describe the main features of feedback control theory and its associated techniques, concentrating on its critiques of behavior analysis, as well as the commonalities they share. While some of feedback control theory's major critiques of behavior analysis arise from the fact that they focus on different levels of organization, we believe that some are legitimate and meaningful. Moreover, feedback control theory seems to blend with neurobiology more smoothly as compared to canonical behavior analysis, which only subsists in a scattered handful of fields. If this paradigm shift truly takes place, behavior analysts-whether they accept or reject this new currency-should be mindful of the basics of the feedback control approach.
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Affiliation(s)
| | - Emmanuel Alcalá
- Instituto Tecnológico de Estudios Superiores de Occidente, Guadalajara, México
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Farkhondeh Tale Navi F, Heysieattalab S, Ramanathan DS, Raoufy MR, Nazari MA. Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 2021; 483:104-126. [PMID: 34902494 DOI: 10.1016/j.neuroscience.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022]
Abstract
Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.
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Affiliation(s)
- Farhad Farkhondeh Tale Navi
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | - Soomaayeh Heysieattalab
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | | | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Ali Nazari
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
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Chen ZS, Pesaran B. Improving scalability in systems neuroscience. Neuron 2021; 109:1776-1790. [PMID: 33831347 PMCID: PMC8178195 DOI: 10.1016/j.neuron.2021.03.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA; Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA.
| | - Bijan Pesaran
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neurology, New York University School of Medicine, New York, NY 10016, USA.
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11
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Tanskanen JM, Ahtiainen A, Hyttinen JA. Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review. Bioelectricity 2020; 2:328-347. [PMID: 34471853 PMCID: PMC8370352 DOI: 10.1089/bioe.2020.0028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.
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Affiliation(s)
- Jarno M.A. Tanskanen
- BioMediTech Institute and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Annika Ahtiainen
- BioMediTech Institute and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jari A.K. Hyttinen
- BioMediTech Institute and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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12
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Aicardi C, Akintoye S, Fothergill BT, Guerrero M, Klinker G, Knight W, Klüver L, Morel Y, Morin FO, Stahl BC, Ulnicane I. Ethical and Social Aspects of Neurorobotics. SCIENCE AND ENGINEERING ETHICS 2020; 26:2533-2546. [PMID: 32700245 PMCID: PMC7550362 DOI: 10.1007/s11948-020-00248-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The interdisciplinary field of neurorobotics looks to neuroscience to overcome the limitations of modern robotics technology, to robotics to advance our understanding of the neural system's inner workings, and to information technology to develop tools that support those complementary endeavours. The development of these technologies is still at an early stage, which makes them an ideal candidate for proactive and anticipatory ethical reflection. This article explains the current state of neurorobotics development within the Human Brain Project, originating from a close collaboration between the scientific and technical experts who drive neurorobotics innovation, and the humanities and social sciences scholars who provide contextualising and reflective capabilities. This article discusses some of the ethical issues which can reasonably be expected. On this basis, the article explores possible gaps identified within this collaborative, ethical reflection that calls for attention to ensure that the development of neurorobotics is ethically sound and socially acceptable and desirable.
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Affiliation(s)
| | - Simisola Akintoye
- Institute for Law, Justice and Society, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
| | - B Tyr Fothergill
- Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
| | - Manuel Guerrero
- Centre for Research Ethics & Bioethics (CRB), Uppsala University, Uppsala, Sweden
- Department of Bioethics and Medical Humanities, University of Chile, Santiago, Chile
| | | | - William Knight
- Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
| | - Lars Klüver
- The Danish Board of Technology, Copenhagen, Denmark
| | | | | | - Bernd Carsten Stahl
- Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, UK.
| | - Inga Ulnicane
- Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
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13
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Allegra Mascaro AL, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO, Pavone FS. Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. Front Syst Neurosci 2020; 14:31. [PMID: 32733210 PMCID: PMC7359878 DOI: 10.3389/fnsys.2020.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 05/08/2020] [Indexed: 01/22/2023] Open
Abstract
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
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Affiliation(s)
- Anna Letizia Allegra Mascaro
- Neuroscience Institute, National Research Council, Pisa, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Egidio Falotico
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Spase Petkoski
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Maria Pasquini
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Núria Tort-Colet
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Emilia Conti
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | | | | | | | - Emanuele Formento
- Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Emmanouil Angelidis
- Fortiss GmbH, Munich, Germany.,Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | | | | | - Matteo Caleo
- Neuroscience Institute, National Research Council, Pisa, Italy.,Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Auke Ijspeert
- Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Cecilia Laschi
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Viktor Jirsa
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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14
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A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions. SENSORS 2020; 20:s20102770. [PMID: 32414060 PMCID: PMC7285770 DOI: 10.3390/s20102770] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/13/2020] [Accepted: 05/10/2020] [Indexed: 01/09/2023]
Abstract
Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep's effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with 'sleep and stimulation' and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.
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15
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Skottnik L, Linden DEJ. Mental Imagery and Brain Regulation-New Links Between Psychotherapy and Neuroscience. Front Psychiatry 2019; 10:779. [PMID: 31736799 PMCID: PMC6831624 DOI: 10.3389/fpsyt.2019.00779] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 09/30/2019] [Indexed: 01/23/2023] Open
Abstract
Mental imagery is a promising tool and mechanism of psychological interventions, particularly for mood and anxiety disorders. In parallel developments, neuromodulation techniques have shown promise as add-on therapies in psychiatry, particularly non-invasive brain stimulation for depression. However, these techniques have not yet been combined in a systematic manner. One novel technology that may be able to achieve this is neurofeedback, which entails the self-regulation of activation in specific brain areas or networks (or the self-modulation of distributed activation patterns) by the patients themselves, through real-time feedback of brain activation (for example, from functional magnetic resonance imaging). One of the key mechanisms by which patients learn such self-regulation is mental imagery. Here, we will first review the main mental imagery approaches in psychotherapy and the implicated brain networks. We will then discuss how these networks can be targeted with neuromodulation (neurofeedback or non-invasive or invasive brain stimulation). We will review the clinical evidence for neurofeedback and discuss possible ways of enhancing it through systematic combination with psychological interventions, with a focus on depression, anxiety disorders, and addiction. The overarching aim of this perspective paper will be to open a debate on new ways of developing neuropsychotherapies.
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Affiliation(s)
| | - David E. J. Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
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16
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Khoyratee F, Grassia F, Saïghi S, Levi T. Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization. Front Neurosci 2019; 13:377. [PMID: 31068781 PMCID: PMC6491680 DOI: 10.3389/fnins.2019.00377] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 04/02/2019] [Indexed: 01/04/2023] Open
Abstract
Neurological diseases can be studied by performing bio-hybrid experiments using a real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a set of equations including biophysical parameters which can serve as a base to represent different classes of neurons and affected cells. Also, connecting the artificial neurons to the biological cells would allow us to understand the effect of the SNN stimulation using different parameters on nerve cells. Thus, designing a real-time SNN could useful for the study of simulations of some part of the brain. Here, we present a different approach to optimize the Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) implementation. The equations of the conductance have been unified to allow the use of same functions with different parameters for all ionic channels. The low resources and high-speed implementation also include features, such as synaptic noise using the Ornstein-Uhlenbeck process and different synapse receptors including AMPA, GABAa, GABAb, and NMDA receptors. The platform allows real-time modification of the neuron parameters and can output different cortical neuron families like Fast Spiking (FS), Regular Spiking (RS), Intrinsically Bursting (IB), and Low Threshold Spiking (LTS) neurons using a Digital to Analog Converter (DAC). Gaussian distribution of the synaptic noise highlights similarities with the biological noise. Also, cross-correlation between the implementation and the model shows strong correlations, and bifurcation analysis reproduces similar behavior compared to the original Hodgkin-Huxley model. The implementation of one core of calculation uses 3% of resources of the FPGA and computes in real-time 500 neurons with 25,000 synapses and synaptic noise which can be scaled up to 15,000 using all resources. This is the first step toward neuromorphic system which can be used for the simulation of bio-hybridization and for the study of neurological disorders or the advanced research on neuroprosthesis to regain lost function.
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Affiliation(s)
- Farad Khoyratee
- Laboratoire de l'Intégration du Matériau au Système, Bordeaux INP, CNRS UMR 5218, University of Bordeaux, Talence, France
| | - Filippo Grassia
- LTI Laboratory, EA 3899, University of Picardie Jules Verne, Amiens, France
| | - Sylvain Saïghi
- Laboratoire de l'Intégration du Matériau au Système, Bordeaux INP, CNRS UMR 5218, University of Bordeaux, Talence, France
| | - Timothée Levi
- Laboratoire de l'Intégration du Matériau au Système, Bordeaux INP, CNRS UMR 5218, University of Bordeaux, Talence, France.,Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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17
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Amaducci R, Reyes-Sanchez M, Elices I, Rodriguez FB, Varona P. RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience. Front Neuroinform 2019; 13:11. [PMID: 30914940 PMCID: PMC6423167 DOI: 10.3389/fninf.2019.00011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/14/2019] [Indexed: 12/05/2022] Open
Abstract
Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.
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Affiliation(s)
- Rodrigo Amaducci
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
| | | | | | | | - Pablo Varona
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
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18
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Skottnik L, Sorger B, Kamp T, Linden D, Goebel R. Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum. Brain Behav 2019; 9:e01240. [PMID: 30790474 PMCID: PMC6422826 DOI: 10.1002/brb3.1240] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/24/2019] [Accepted: 01/25/2019] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86). METHODS To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region. RESULTS During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation. CONCLUSION Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.
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Affiliation(s)
- Leon Skottnik
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, Netherlands.,Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - David Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom.,School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands.,Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, Netherlands
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19
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Doruk RO, Zhang K. Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models. Front Neural Circuits 2019; 12:119. [PMID: 30723397 PMCID: PMC6349832 DOI: 10.3389/fncir.2018.00119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 12/17/2018] [Indexed: 11/26/2022] Open
Abstract
We present an adaptive stimulus design method for efficiently estimating the parameters of a dynamic recurrent network model with interacting excitatory and inhibitory neuronal populations. Although stimuli that are optimized for model parameter estimation should, in theory, have advantages over nonadaptive random stimuli, in practice it remains unclear in what way and to what extent an optimal design of time-varying stimuli may actually improve parameter estimation for this common type of recurrent network models. Here we specified the time course of each stimulus by a Fourier series whose amplitudes and phases were determined by maximizing a utility function based on the Fisher information matrix. To facilitate the optimization process, we have derived differential equations that govern the time evolution of the gradients of the utility function with respect to the stimulus parameters. The network parameters were estimated by maximum likelihood from the spike train data generated by an inhomogeneous Poisson process from the continuous network state. The adaptive design process was repeated in a closed loop, alternating between optimal stimulus design and parameter estimation from the updated stimulus-response data. Our results confirmed that, compared with random stimuli, optimally designed stimuli elicited responses with significantly better likelihood values for parameter estimation. Furthermore, all individual parameters, including the time constants and the connection weights, were recovered more accurately by the optimal design method. We also examined how the errors of different parameter estimates were correlated, and proposed heuristic formulas to account for the correlation patterns by an approximate parameter-confounding theory. Our results suggest that although adaptive optimal stimulus design incurs considerable computational cost even for the simplest excitatory-inhibitory recurrent network model, it may potentially help save time in experiments by reducing the number of stimuli needed for network parameter estimation.
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Affiliation(s)
- R Ozgur Doruk
- Department of Electrical and Electronic Engineering, Atilim University, Golbasi, Turkey.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kechen Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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20
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Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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21
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Ciliberti D, Michon F, Kloosterman F. Real-time classification of experience-related ensemble spiking patterns for closed-loop applications. eLife 2018; 7:36275. [PMID: 30373716 PMCID: PMC6207426 DOI: 10.7554/elife.36275] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 09/27/2018] [Indexed: 02/06/2023] Open
Abstract
Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.
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Affiliation(s)
- Davide Ciliberti
- Neuro-Electronics Research Flanders, Leuven, Belgium.,Brain and Cognition, KU Leuven, Leuven, Belgium.,VIB, Leuven, Belgium
| | - Frédéric Michon
- Neuro-Electronics Research Flanders, Leuven, Belgium.,Brain and Cognition, KU Leuven, Leuven, Belgium.,VIB, Leuven, Belgium
| | - Fabian Kloosterman
- Neuro-Electronics Research Flanders, Leuven, Belgium.,Brain and Cognition, KU Leuven, Leuven, Belgium.,VIB, Leuven, Belgium.,imec, Leuven, Belgium
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22
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Seu GP, Angotzi GN, Boi F, Raffo L, Berdondini L, Meloni P. Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:839-850. [PMID: 29993584 DOI: 10.1109/tbcas.2018.2830659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq-all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems.
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23
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Ciliberti D, Kloosterman F. Falcon: a highly flexible open-source software for closed-loop neuroscience. J Neural Eng 2018; 14:045004. [PMID: 28548044 DOI: 10.1088/1741-2552/aa7526] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Closed-loop experiments provide unique insights into brain dynamics and function. To facilitate a wide range of closed-loop experiments, we created an open-source software platform that enables high-performance real-time processing of streaming experimental data. APPROACH We wrote Falcon, a C++ multi-threaded software in which the user can load and execute an arbitrary processing graph. Each node of a Falcon graph is mapped to a single thread and nodes communicate with each other through thread-safe buffers. The framework allows for easy implementation of new processing nodes and data types. Falcon was tested both on a 32-core and a 4-core workstation. Streaming data was read from either a commercial acquisition system (Neuralynx) or the open-source Open Ephys hardware, while closed-loop TTL pulses were generated with a USB module for digital output. We characterized the round-trip latency of our Falcon-based closed-loop system, as well as the specific latency contribution of the software architecture, by testing processing graphs with up to 32 parallel pipelines and eight serial stages. We finally deployed Falcon in a task of real-time detection of population bursts recorded live from the hippocampus of a freely moving rat. MAIN RESULTS On Neuralynx hardware, round-trip latency was well below 1 ms and stable for at least 1 h, while on Open Ephys hardware latencies were below 15 ms. The latency contribution of the software was below 0.5 ms. Round-trip and software latencies were similar on both 32- and 4-core workstations. Falcon was used successfully to detect population bursts online with ~40 ms average latency. SIGNIFICANCE Falcon is a novel open-source software for closed-loop neuroscience. It has sub-millisecond intrinsic latency and gives the experimenter direct control of CPU resources. We envisage Falcon to be a useful tool to the neuroscientific community for implementing a wide variety of closed-loop experiments, including those requiring use of complex data structures and real-time execution of computationally intensive algorithms, such as population neural decoding/encoding from large cell assemblies.
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Affiliation(s)
- Davide Ciliberti
- Neuro-Electronic Research Flanders (NERF), Leuven, Belgium. Brain & Cognition Research Unit, KU Leuven, Belgium. VIB, Leuven, Belgium
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24
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Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput Biol 2017; 13:e1005842. [PMID: 29131818 PMCID: PMC5703587 DOI: 10.1371/journal.pcbi.1005842] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.
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Affiliation(s)
- Gonzalo E. Mena
- Statistics Department, Columbia University, New York, New York, United States of America
| | - Lauren E. Grosberg
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Sasidhar Madugula
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Paweł Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland
| | - Alan Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - John Cunningham
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - E. J. Chichilnisky
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Liam Paninski
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
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25
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Abstract
The dynamic clamp should be a standard part of every cellular electrophysiologist’s toolbox. That it is not, even 25 years after its introduction, comes down to three issues: money, the disruption that adding dynamic clamp to an existing electrophysiology rig entails, and the technical prowess required of experimenters. These have been valid and limiting issues in the past, but no longer. Technological advances associated with the so-called maker movement render them moot. We demonstrate this by implementing a fast (∼100 kHz) dynamic clamp system using an inexpensive microcontroller (Teensy 3.6). The overall cost of the system is less than USD$100, and assembling it requires no prior electronics experience. Modifying it—for example, to add Hodgkin–Huxley-style conductances—requires no prior programming experience. The system works together with existing electrophysiology data acquisition systems (for Macintosh, Windows, and Linux); it does not attempt to supplant them. Moreover, the process of assembling, modifying, and using the system constitutes a useful pedagogical exercise for students and researchers with no background but an interest in electronics and programming. We demonstrate the system’s utility by implementing conductances as fast as a transient sodium conductance and as complex as the Ornstein–Uhlenbeck conductances of the “point conductance” model of synaptic background activity.
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Hazan H, Ziv NE. Closed Loop Experiment Manager (CLEM)-An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments. Front Neurosci 2017; 11:579. [PMID: 29093659 PMCID: PMC5651259 DOI: 10.3389/fnins.2017.00579] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 10/03/2017] [Indexed: 11/13/2022] Open
Abstract
There is growing need for multichannel electrophysiological systems that record from and interact with neuronal systems in near real-time. Such systems are needed, for example, for closed loop, multichannel electrophysiological/optogenetic experimentation in vivo and in a variety of other neuronal preparations, or for developing and testing neuro-prosthetic devices, to name a few. Furthermore, there is a need for such systems to be inexpensive, reliable, user friendly, easy to set-up, open and expandable, and possess long life cycles in face of rapidly changing computing environments. Finally, they should provide powerful, yet reasonably easy to implement facilities for developing closed-loop protocols for interacting with neuronal systems. Here, we survey commercial and open source systems that address these needs to varying degrees. We then present our own solution, which we refer to as Closed Loop Experiments Manager (CLEM). CLEM is an open source, soft real-time, Microsoft Windows desktop application that is based on a single generic personal computer (PC) and an inexpensive, general-purpose data acquisition board. CLEM provides a fully functional, user-friendly graphical interface, possesses facilities for recording, presenting and logging electrophysiological data from up to 64 analog channels, and facilities for controlling external devices, such as stimulators, through digital and analog interfaces. Importantly, it includes facilities for running closed-loop protocols written in any programming language that can generate dynamic link libraries (DLLs). We describe the application, its architecture and facilities. We then demonstrate, using networks of cortical neurons growing on multielectrode arrays (MEA) that despite its reliance on generic hardware, its performance is appropriate for flexible, closed-loop experimentation at the neuronal network level.
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Affiliation(s)
- Hananel Hazan
- Faculty of Medicine, Technion, Haifa, Israel.,Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel
| | - Noam E Ziv
- Faculty of Medicine, Technion, Haifa, Israel.,Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel
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Gerhard F, Deger M, Truccolo W. On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs. PLoS Comput Biol 2017; 13:e1005390. [PMID: 28234899 PMCID: PMC5325182 DOI: 10.1371/journal.pcbi.1005390] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 01/28/2017] [Indexed: 01/12/2023] Open
Abstract
Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.
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Affiliation(s)
- Felipe Gerhard
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | - Moritz Deger
- School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Institute for Zoology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
- Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Center for Neurorestoration & Neurotechnology, U. S. Department of Veterans Affairs, Providence, Rhode Island, United States of America
- * E-mail:
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28
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Lajoie G, Krouchev NI, Kalaska JF, Fairhall AL, Fetz EE. Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface. PLoS Comput Biol 2017; 13:e1005343. [PMID: 28151957 PMCID: PMC5313237 DOI: 10.1371/journal.pcbi.1005343] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Revised: 02/16/2017] [Accepted: 01/03/2017] [Indexed: 12/19/2022] Open
Abstract
Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.
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Affiliation(s)
- Guillaume Lajoie
- University of Washington Institute for Neuroengineering, University of Washington, Seattle, WA, USA
| | | | - John F. Kalaska
- Groupe de recherche sur le système nerveux central, Département de neurosciences, Université de Montreal, Montreal, QC, Canada
| | - Adrienne L. Fairhall
- University of Washington Institute for Neuroengineering, University of Washington, Seattle, WA, USA
- Dept. of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Dept. of Physics, University of Washington, Seattle, WA, USA
| | - Eberhard E. Fetz
- University of Washington Institute for Neuroengineering, University of Washington, Seattle, WA, USA
- Dept. of Physiology and Biophysics, University of Washington, Seattle, WA, USA
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29
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Gerasimenko Y, Sayenko D, Gad P, Liu CT, Tillakaratne NJK, Roy RR, Kozlovskaya I, Edgerton VR. Feed-Forwardness of Spinal Networks in Posture and Locomotion. Neuroscientist 2016; 23:441-453. [PMID: 28403746 DOI: 10.1177/1073858416683681] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We present a new perspective on the concept of feed-forward compared to feedback mechanisms for motor control. We propose that conceptually all sensory information in real time provided to the brain and spinal cord can be viewed as a feed-forward phenomenon. We also propose that the spinal cord continually adapts to a broad array of ongoing sensory information that is used to adjust the probability of making timely and predictable decisions of selected networks that will execute a given response. One interpretation of the term feedback historically entails responses with short delays. We propose that feed-forward mechanisms, however, range in timeframes of milliseconds to an evolutionary perspective, that is, "evolutionary learning." Continuously adapting events enable a high level of automaticity within the sensorimotor networks that mediate "planned" motor tasks. We emphasize that either a very small or a very large proportion of motor responses can be under some level of conscious vs automatic control. Furthermore, we make a case that a major component of automaticity of the neural control of movement in vertebrates is located within spinal cord networks. Even without brain input, the spinal cord routinely uses feed-forward processing of sensory information, particularly proprioceptive and cutaneous, to continuously make fundamental decisions that define motor responses. In effect, these spinal networks may be largely responsible for executing coordinated sensorimotor tasks, even those under normal "conscious" control.
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Affiliation(s)
- Yury Gerasimenko
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,2 Pavlov Institute of Physiology, St. Petersburg, Russia.,3 Russian Federation State Scientific Center, Institute for Bio-Medical Problems, Russian Academy of Sciences, Moscow, Russia.,4 Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Dimitry Sayenko
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Parag Gad
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chao-Tuan Liu
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Niranjala J K Tillakaratne
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,5 Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Roland R Roy
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,5 Brain Research Institute, University of California, Los Angeles, CA, USA
| | | | - V Reggie Edgerton
- 1 Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,5 Brain Research Institute, University of California, Los Angeles, CA, USA.,6 Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,7 Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,8 Institute Guttmann. Hospital de Neurorehabilitació, Institut Universitari adscrit a la Universitat Autònoma de Barcelona, Badalona, Spain
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Lareo A, Forlim CG, Pinto RD, Varona P, Rodriguez FDB. Temporal Code-Driven Stimulation: Definition and Application to Electric Fish Signaling. Front Neuroinform 2016; 10:41. [PMID: 27766078 PMCID: PMC5052257 DOI: 10.3389/fninf.2016.00041] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 09/21/2016] [Indexed: 11/18/2022] Open
Abstract
Closed-loop activity-dependent stimulation is a powerful methodology to assess information processing in biological systems. In this context, the development of novel protocols, their implementation in bioinformatics toolboxes and their application to different description levels open up a wide range of possibilities in the study of biological systems. We developed a methodology for studying biological signals representing them as temporal sequences of binary events. A specific sequence of these events (code) is chosen to deliver a predefined stimulation in a closed-loop manner. The response to this code-driven stimulation can be used to characterize the system. This methodology was implemented in a real time toolbox and tested in the context of electric fish signaling. We show that while there are codes that evoke a response that cannot be distinguished from a control recording without stimulation, other codes evoke a characteristic distinct response. We also compare the code-driven response to open-loop stimulation. The discussed experiments validate the proposed methodology and the software toolbox.
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Affiliation(s)
- Angel Lareo
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica superior, Universidad Autónoma de MadridMadrid, Spain
| | - Caroline G. Forlim
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent UniversityGhent, Belgium
| | - Reynaldo D. Pinto
- Laboratory of Neurodynamics/Neurobiophysics, Department of Physics and Interdisciplinary Sciences, Institute of Physics of São Carlos, Universidade de São PauloSão Paulo, Brazil
| | - Pablo Varona
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica superior, Universidad Autónoma de MadridMadrid, Spain
| | - Francisco de Borja Rodriguez
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica superior, Universidad Autónoma de MadridMadrid, Spain
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Zrenner C, Belardinelli P, Müller-Dahlhaus F, Ziemann U. Closed-Loop Neuroscience and Non-Invasive Brain Stimulation: A Tale of Two Loops. Front Cell Neurosci 2016; 10:92. [PMID: 27092055 PMCID: PMC4823269 DOI: 10.3389/fncel.2016.00092] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 03/23/2016] [Indexed: 12/31/2022] Open
Abstract
Closed-loop neuroscience is receiving increasing attention with recent technological advances that enable complex feedback loops to be implemented with millisecond resolution on commodity hardware. We summarize emerging conceptual and methodological frameworks that are available to experimenters investigating a “brain in the loop” using non-invasive brain stimulation and briefly review the experimental and therapeutic implications. We take the view that closed-loop neuroscience in fact deals with two conceptually quite different loops: a “brain-state dynamics” loop, used to couple with and modulate the trajectory of neuronal activity patterns, and a “task dynamics” loop, that is the bidirectional motor-sensory interaction between brain and (simulated) environment, and which enables goal-directed behavioral tasks to be incorporated. Both loops need to be considered and combined to realize the full experimental and therapeutic potential of closed-loop neuroscience.
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Affiliation(s)
- Christoph Zrenner
- Brain Networks and Plasticity Laboratory, Department of Neurology and Stroke and Hertie-Institute for Clinical Brain Research, University of Tübingen Tübingen, Germany
| | - Paolo Belardinelli
- Brain Networks and Plasticity Laboratory, Department of Neurology and Stroke and Hertie-Institute for Clinical Brain Research, University of Tübingen Tübingen, Germany
| | - Florian Müller-Dahlhaus
- Brain Networks and Plasticity Laboratory, Department of Neurology and Stroke and Hertie-Institute for Clinical Brain Research, University of Tübingen Tübingen, Germany
| | - Ulf Ziemann
- Brain Networks and Plasticity Laboratory, Department of Neurology and Stroke and Hertie-Institute for Clinical Brain Research, University of Tübingen Tübingen, Germany
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Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. J Neurosci Methods 2015; 258:1-15. [PMID: 26529367 DOI: 10.1016/j.jneumeth.2015.10.010] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 09/17/2015] [Accepted: 10/20/2015] [Indexed: 11/24/2022]
Abstract
BACKGROUND There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately. NEW METHOD Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements. RESULTS We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration. COMPARISON WITH EXISTING METHODS DMD is closely related to principal components analysis (PCA) and discrete Fourier transform (DFT). We may think of DMD as a rotation of the low-dimensional PCA space such that each basis vector has coherent dynamics. CONCLUSIONS The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings.
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Biró I, Giugliano M. A reconfigurable visual-programming library for real-time closed-loop cellular electrophysiology. Front Neuroinform 2015; 9:17. [PMID: 26157385 PMCID: PMC4477165 DOI: 10.3389/fninf.2015.00017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Accepted: 06/09/2015] [Indexed: 12/04/2022] Open
Abstract
Most of the software platforms for cellular electrophysiology are limited in terms of flexibility, hardware support, ease of use, or re-configuration and adaptation for non-expert users. Moreover, advanced experimental protocols requiring real-time closed-loop operation to investigate excitability, plasticity, dynamics, are largely inaccessible to users without moderate to substantial computer proficiency. Here we present an approach based on MATLAB/Simulink, exploiting the benefits of LEGO-like visual programming and configuration, combined to a small, but easily extendible library of functional software components. We provide and validate several examples, implementing conventional and more sophisticated experimental protocols such as dynamic-clamp or the combined use of intracellular and extracellular methods, involving closed-loop real-time control. The functionality of each of these examples is demonstrated with relevant experiments. These can be used as a starting point to create and support a larger variety of electrophysiological tools and methods, hopefully extending the range of default techniques and protocols currently employed in experimental labs across the world.
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Affiliation(s)
- István Biró
- Theoretical Neurobiology and Neuroengineering, University of AntwerpAntwerpen, Belgium
| | - Michele Giugliano
- Theoretical Neurobiology and Neuroengineering, University of AntwerpAntwerpen, Belgium
- Department of Computer Science, University of SheffieldSheffield, UK
- Laboratory for Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
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