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Xu T, Xiao N, Zhai X, Kwan Chan P, Tin C. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning. J Neural Eng 2019; 15:016021. [PMID: 29115280 DOI: 10.1088/1741-2552/aa98e9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). APPROACH The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. MAIN RESULTS This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. SIGNIFICANCE This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
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Affiliation(s)
- Tao Xu
- Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, SAR, People's Republic of China
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Ramirez-Zamora A, Giordano JJ, Gunduz A, Brown P, Sanchez JC, Foote KD, Almeida L, Starr PA, Bronte-Stewart HM, Hu W, McIntyre C, Goodman W, Kumsa D, Grill WM, Walker HC, Johnson MD, Vitek JL, Greene D, Rizzuto DS, Song D, Berger TW, Hampson RE, Deadwyler SA, Hochberg LR, Schiff ND, Stypulkowski P, Worrell G, Tiruvadi V, Mayberg HS, Jimenez-Shahed J, Nanda P, Sheth SA, Gross RE, Lempka SF, Li L, Deeb W, Okun MS. Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front Neurosci 2018; 11:734. [PMID: 29416498 PMCID: PMC5787550 DOI: 10.3389/fnins.2017.00734] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/15/2017] [Indexed: 12/21/2022] Open
Abstract
The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.
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Affiliation(s)
- Adolfo Ramirez-Zamora
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States,*Correspondence: Adolfo Ramirez-Zamora
| | - James J. Giordano
- Department of Neurology, Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Justin C. Sanchez
- Biological Technologies Office, Defense Advanced Research Projects Agency, Arlington, VA, United States
| | - Kelly D. Foote
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Leonardo Almeida
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Philip A. Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Helen M. Bronte-Stewart
- Departments of Neurology and Neurological Sciences and Neurosurgery, Stanford University, Stanford, CA, United States
| | - Wei Hu
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Cameron McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Wayne Goodman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Doe Kumsa
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, White Oak Federal Research Center, Silver Spring, MD, United States
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Harrison C. Walker
- Division of Movement Disorders, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States,Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Daniel S. Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert E. Hampson
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Sam A. Deadwyler
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Leigh R. Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States,Center for Neurorestoration and Neurotechnology, Rehabilitation R and D Service, Veterans Affairs Medical Center, Providence, RI, United States,School of Engineering and Brown Institute for Brain Science, Brown University, Providence, RI, United States
| | - Nicholas D. Schiff
- Laboratory of Cognitive Neuromodulation, Feil Family Brain Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Vineet Tiruvadi
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Helen S. Mayberg
- Departments of Psychiatry, Neurology, and Radiology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Joohi Jimenez-Shahed
- Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Pranav Nanda
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Sameer A. Sheth
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Robert E. Gross
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
| | - Scott F. Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Beijing, China,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Wissam Deeb
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
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Ienca M, Jotterand F, Elger BS. From Healthcare to Warfare and Reverse: How Should We Regulate Dual-Use Neurotechnology? Neuron 2018; 97:269-274. [DOI: 10.1016/j.neuron.2017.12.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 11/11/2017] [Accepted: 12/10/2017] [Indexed: 11/29/2022]
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Abstract
Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al. 2011) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995), we demonstrate the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control.
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Affiliation(s)
- P.D. Watson
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
| | - K. M. Horecka
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
| | - N.J. Cohen
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
- Department of Psychology, UIUC, IL, USA
| | - R. Ratnam
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Coordinated Science Laboratory, UIUC, Urbana, IL, USA
- Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore
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Deadwyler SA, Hampson RE, Song D, Opris I, Gerhardt GA, Marmarelis VZ, Berger TW. A cognitive prosthesis for memory facilitation by closed-loop functional ensemble stimulation of hippocampal neurons in primate brain. Exp Neurol 2016; 287:452-460. [PMID: 27233622 DOI: 10.1016/j.expneurol.2016.05.031] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 05/21/2016] [Accepted: 05/23/2016] [Indexed: 11/25/2022]
Abstract
Very productive collaborative investigations characterized how multineuron hippocampal ensembles recorded in nonhuman primates (NHPs) encode short-term memory necessary for successful performance in a delayed match to sample (DMS) task and utilized that information to devise a unique nonlinear multi-input multi-output (MIMO) memory prosthesis device to enhance short-term memory in real-time during task performance. Investigations have characterized how the hippocampus in primate brain encodes information in a multi-item, rule-controlled, delayed match to sample (DMS) task. The MIMO model was applied via closed loop feedback micro-current stimulation during the task via conformal electrode arrays and enhanced performance of the complex memory requirements. These findings clearly indicate detection of a means by which the hippocampus encodes information and transmits this information to other brain regions involved in memory processing. By employing the nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded multi-neuron CA1 and CA3 activity, it was possible to extract information encoded in the Sample phase of DMS trials that was necessary for successful performance in the subsequent Match phase of the task. The extension of this MIMO model to online delivery of electrical stimulation patterns to the same recording loci that exhibited successful CA1 firing in the DMS Sample Phase provided the means to increase task performance on a trial-by-trial basis. Increased utility of the MIMO model as a memory prosthesis was exhibited by the demonstration of cumulative increases in DMS task performance with repeated MIMO stimulation over many sessions. These results, reported below in this article, provide the necessary demonstrations to further the feasibility of the MIMO model as a memory prosthesis to recover and/or enhance encoding of cognitive information in humans with memory disruptions resulting from brain injury, disease or aging.
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Opris I, Santos LM, Gerhardt GA, Song D, Berger TW, Hampson RE, Deadwyler SA. Distributed encoding of spatial and object categories in primate hippocampal microcircuits. Front Neurosci 2015; 9:317. [PMID: 26500473 PMCID: PMC4594006 DOI: 10.3389/fnins.2015.00317] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/24/2015] [Indexed: 11/16/2022] Open
Abstract
The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics.
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Affiliation(s)
- Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Lucas M. Santos
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Greg A. Gerhardt
- Department of Anatomy and Neurobiology, University of KentuckyLexington, KY, USA
| | - Dong Song
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
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7
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Fetterhoff D, Kraft RA, Sandler RA, Opris I, Sexton CA, Marmarelis VZ, Hampson RE, Deadwyler SA. Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences. Front Syst Neurosci 2015; 9:130. [PMID: 26441562 PMCID: PMC4585000 DOI: 10.3389/fnsys.2015.00130] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 09/03/2015] [Indexed: 11/15/2022] Open
Abstract
Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.
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Affiliation(s)
- Dustin Fetterhoff
- Neuroscience Program, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Robert A Kraft
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Roman A Sandler
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Cheryl A Sexton
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
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8
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Miranda RA, Casebeer WD, Hein AM, Judy JW, Krotkov EP, Laabs TL, Manzo JE, Pankratz KG, Pratt GA, Sanchez JC, Weber DJ, Wheeler TL, Ling GS. DARPA-funded efforts in the development of novel brain–computer interface technologies. J Neurosci Methods 2015; 244:52-67. [PMID: 25107852 DOI: 10.1016/j.jneumeth.2014.07.019] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Revised: 07/08/2014] [Accepted: 07/24/2014] [Indexed: 02/01/2023]
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Bursting regimes in a reaction-diffusion system with action potential-dependent equilibrium. PLoS One 2015; 10:e0122401. [PMID: 25823018 PMCID: PMC4379183 DOI: 10.1371/journal.pone.0122401] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 02/14/2015] [Indexed: 11/30/2022] Open
Abstract
The equilibrium Nernst potential plays a critical role in neural cell dynamics. A common approximation used in studying electrical dynamics of excitable cells is that the ionic concentrations inside and outside the cell membranes act as charge reservoirs and remain effectively constant during excitation events. Research into brain electrical activity suggests that relaxing this assumption may provide a better understanding of normal and pathophysiological functioning of the brain. In this paper we explore time-dependent ionic concentrations by allowing the ion-specific Nernst potentials to vary with developing transmembrane potential. As a specific implementation, we incorporate the potential-dependent Nernst shift into a one-dimensional Morris-Lecar reaction-diffusion model. Our main findings result from a region in parameter space where self-sustaining oscillations occur without external forcing. Studying the system close to the bifurcation boundary, we explore the vulnerability of the system with respect to external stimulations which disrupt these oscillations and send the system to a stable equilibrium. We also present results for an extended, one-dimensional cable of excitable tissue tuned to this parameter regime and stimulated, giving rise to complex spatiotemporal pattern formation. Potential applications to the emergence of neuronal bursting in similar two-variable systems and to pathophysiological seizure-like activity are discussed.
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Deadwyler SA, Berger TW, Opris I, Song D, Hampson RE. Neurons and networks organizing and sequencing memories. Brain Res 2014; 1621:335-44. [PMID: 25553617 DOI: 10.1016/j.brainres.2014.12.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 12/16/2014] [Accepted: 12/17/2014] [Indexed: 01/23/2023]
Abstract
Hippocampal CA1 and CA3 neurons sampled randomly in large numbers in primate brain show conclusive examples of hierarchical encoding of task specific information. Hierarchical encoding allows multi-task utilization of the same hippocampal neural networks via distributed firing between neurons that respond to subsets, attributes or "categories" of stimulus features which can be applied in events in different contexts. In addition, such networks are uniquely adaptable to neural systems unrestricted by rigid synaptic architecture (i.e. columns, layers or "patches") which physically limits the number of possible task-specific interactions between neurons. Also hierarchical encoding is not random; it requires multiple exposures to the same types of relevant events to elevate synaptic connectivity between neurons for different stimulus features that occur in different task-dependent contexts. The large number of cells within associated hierarchical circuits in structures such as hippocampus provides efficient processing of information relevant to common memory-dependent behavioral decisions within different contextual circumstances. This article is part of a Special Issue entitled SI: Brain and Memory.
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Affiliation(s)
- Sam A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA.
| | - Theodore W Berger
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
| | - Dong Song
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
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Song D, Chan RHM, Robinson BS, Marmarelis VZ, Opris I, Hampson RE, Deadwyler SA, Berger TW. Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling. J Neurosci Methods 2014; 244:123-35. [PMID: 25280984 DOI: 10.1016/j.jneumeth.2014.09.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Revised: 09/23/2014] [Accepted: 09/23/2014] [Indexed: 11/30/2022]
Abstract
This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input-output nonstationarity as the consequence of the past input-output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.
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Affiliation(s)
- Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Rosa H M Chan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Brian S Robinson
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Sam A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
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12
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Sandler RA, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Model-based asessment of an in-vivo predictive relationship from CA1 to CA3 in the rodent hippocampus. J Comput Neurosci 2014; 38:89-103. [PMID: 25260381 DOI: 10.1007/s10827-014-0530-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Revised: 09/02/2014] [Accepted: 09/05/2014] [Indexed: 01/02/2023]
Abstract
Although an anatomical connection from CA1 to CA3 via the Entorhinal Cortex (EC) and through backprojecting interneurons has long been known it exist, it has never been examined quantitatively on the single neuron level, in the in-vivo nonpatholgical, nonperturbed brain. Here, single spike activity was recorded using a multi-electrode array from the CA3 and CA1 areas of the rodent hippocampus (N = 7) during a behavioral task. The predictive power from CA3→CA1 and CA1→CA3 was examined by constructing Multivariate Autoregressive (MVAR) models from recorded neurons in both directions. All nonsignificant inputs and models were identified and removed by means of Monte Carlo simulation methods. It was found that 121/166 (73 %) CA3→CA1 models and 96/145 (66 %) CA1→CA3 models had significant predictive power, thus confirming a predictive 'Granger' causal relationship from CA1 to CA3. This relationship is thought to be caused by a combination of truly causal connections such as the CA1→EC→CA3 pathway and common inputs such as those from the Septum. All MVAR models were then examined in the frequency domain and it was found that CA3 kernels had significantly more power in the theta and beta range than those of CA1, confirming CA3's role as an endogenous hippocampal pacemaker.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, DRB 367, 1042 Downey Way Los Angeles, Los Angeles, CA, 90089, USA,
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13
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Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration. J Neurosci Methods 2014; 244:136-53. [PMID: 25086297 DOI: 10.1016/j.jneumeth.2014.07.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 06/06/2014] [Accepted: 07/16/2014] [Indexed: 12/22/2022]
Abstract
BACKGROUND Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. NEW METHOD Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. RESULTS Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. COMPARISON WITH EXISTING METHODS WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. CONCLUSION z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.
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Deadwyler SA, Berger TW, Sweatt AJ, Song D, Chan RHM, Opris I, Gerhardt GA, Marmarelis VZ, Hampson RE. Donor/recipient enhancement of memory in rat hippocampus. Front Syst Neurosci 2013; 7:120. [PMID: 24421759 PMCID: PMC3872745 DOI: 10.3389/fnsys.2013.00120] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 12/06/2013] [Indexed: 11/13/2022] Open
Abstract
The critical role of the mammalian hippocampus in the formation, translation and retrieval of memory has been documented over many decades. There are many theories of how the hippocampus operates to encode events and a precise mechanism was recently identified in rats performing a short-term memory task which demonstrated that successful information encoding was promoted via specific patterns of activity generated within ensembles of hippocampal neurons. In the study presented here, these “representations” were extracted via a customized non-linear multi-input multi-output (MIMO) mathematical model which allowed prediction of successful performance on specific trials within the testing session. A unique feature of this characterization was demonstrated when successful information encoding patterns were derived online from well-trained “donor” animals during difficult long-delay trials and delivered via online electrical stimulation to synchronously tested naïve “recipient” animals never before exposed to the delay feature of the task. By transferring such model-derived trained (donor) animal hippocampal firing patterns via stimulation to coupled naïve recipient animals, their task performance was facilitated in a direct “donor-recipient” manner. This provides the basis for utilizing extracted appropriate neural information from one brain to induce, recover, or enhance memory related processing in the brain of another subject.
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Affiliation(s)
- Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Andrew J Sweatt
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Rosa H M Chan
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Greg A Gerhardt
- Department of Neurobiology, Chandler Medical School, University of Kentucky Lexington, KY, USA
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
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Hampson RE, Song D, Opris I, Santos LM, Shin DC, Gerhardt GA, Marmarelis VZ, Berger TW, Deadwyler SA. Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing. J Neural Eng 2013; 10:066013. [PMID: 24216292 PMCID: PMC3919468 DOI: 10.1088/1741-2560/10/6/066013] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer's, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. APPROACH NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. MAIN RESULTS The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. SIGNIFICANCE These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.
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Affiliation(s)
- Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Lucas M. Santos
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Dae C. Shin
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Greg A. Gerhardt
- Department of Anatomy and Neurobiology, University of Kentucky, Lexington, KY
| | | | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
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Marmarelis VZ, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW. On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes. J Comput Neurosci 2013; 36:321-37. [PMID: 23929124 DOI: 10.1007/s10827-013-0475-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 07/16/2013] [Accepted: 07/17/2013] [Indexed: 11/25/2022]
Abstract
Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.
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Affiliation(s)
- V Z Marmarelis
- Department of Biomedical Engineering and the Biomedical Simulations Resource (BMSR), University of Southern California, Los Angeles, CA, 90089, USA,
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Marmarelis VZ, Shin DC, Hampson RE, Deadwyler SA, Song D, Berger TW. Design of optimal stimulation patterns for neuronal ensembles based on Volterra-type hierarchical modeling. J Neural Eng 2012; 9:066003. [PMID: 23075519 DOI: 10.1088/1741-2560/9/6/066003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This paper presents a general methodology for the optimal design of stimulation patterns applied to neuronal ensembles in order to elicit a desired effect. The methodology follows a variant of the hierarchical Volterra modeling approach that utilizes input-output data to construct predictive models that describe the effects of interactions among multiple input events in an ascending order of interaction complexity. The illustrative example presented in this paper concerns the multi-unit activity of CA1 neurons in the hippocampus of a rodent performing a learned delayed-nonmatch-to-sample (DNMS) task. The multi-unit activity of the hippocampal CA1 neurons is recorded via chronically implanted multi-electrode arrays during this task. The obtained model quantifies the likelihood of having correct performance of the specific task for a given multi-unit (spatiotemporal) activity pattern of a CA1 neuronal ensemble during the 'sample presentation' phase of the DNMS task. The model can be used to determine computationally (off-line) the 'optimal' multi-unit stimulation pattern that maximizes the likelihood of inducing the correct performance of the DNMS task. Our working hypothesis is that application of this optimal stimulation pattern will enhance performance of the DNMS task due to enhancement of memory formation and storage during the 'sample presentation' phase of the task.
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Affiliation(s)
- V Z Marmarelis
- Department of Biomedical Engineering and the Biomedical Simulations Resource, University of Southern California, Los Angeles, CA 90089, USA.
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Hampson RE, Gerhardt GA, Marmarelis V, Song D, Opris I, Santos L, Berger TW, Deadwyler SA. Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. J Neural Eng 2012; 9:056012. [PMID: 22976769 DOI: 10.1088/1741-2560/9/5/056012] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Maintenance of cognitive control is a major concern for many human disease conditions; therefore, a major goal of human neuroprosthetics is to facilitate and/or recover the cognitive function when such circumstances impair appropriate decision making. APPROACH Minicolumnar activity from the prefrontal cortex (PFC) was recorded from nonhuman primates trained to perform a delayed match to sample (DMS), via custom-designed conformal multielectrode arrays that provided inter-laminar recordings from neurons in the PFC layer 2/3 and layer 5. Such recordings were analyzed via a previously demonstrated nonlinear multi-input-multi-output (MIMO) neuroprosthesis in rodents, which extracted and characterized multicolumnar firing patterns during DMS performance. MAIN RESULTS The MIMO model verified that the conformal recorded individual PFC minicolumns responded to entrained target selections in patterns critical for successful DMS performance. This allowed the substitution of task-related layer 5 neuron firing patterns with electrical stimulation in the same recording regions during columnar transmission from layer 2/3 at the time of target selection. Such stimulation improved normal task performance, but more importantly, recovered performance when applied as a neuroprosthesis following the pharmacological disruption of decision making in the same task. SIGNIFICANCE These findings provide the first successful application of neuroprosthesis in the primate brain designed specifically to restore or repair the disrupted cognitive function.
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Affiliation(s)
- Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations. J Comput Neurosci 2012; 34:163-83. [PMID: 22878687 DOI: 10.1007/s10827-012-0412-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2012] [Revised: 06/04/2012] [Accepted: 06/27/2012] [Indexed: 10/28/2022]
Abstract
We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.
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