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Wei Y, Marshall AG, McGlone FP, Makdani A, Zhu Y, Yan L, Ren L, Wei G. Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control. Nat Commun 2024; 15:6857. [PMID: 39127772 PMCID: PMC11316806 DOI: 10.1038/s41467-024-50616-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 07/12/2024] [Indexed: 08/12/2024] Open
Abstract
In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.
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Affiliation(s)
- Yuyang Wei
- Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
- Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, M13 9PL, UK
| | - Andrew G Marshall
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L69 3BX, UK
| | - Francis P McGlone
- Department of Neuroscience and Biomedical Engineering, Aalto University, Otakaari 24, Helsinki, Finland
| | - Adarsh Makdani
- School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, L3 5UX, UK
| | - Yiming Zhu
- Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, M13 9PL, UK
| | - Lingyun Yan
- Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, M13 9PL, UK
| | - Lei Ren
- Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, M13 9PL, UK.
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Jilin, China.
| | - Guowu Wei
- School of Science, Engineering and Environment, University of Salford, Manchester, M5 4WT, UK.
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2
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Rogers ER, Capogrosso M, Lempka SF. Biophysics of Frequency-Dependent Variation in Paresthesia and Pain Relief during Spinal Cord Stimulation. J Neurosci 2024; 44:e2199232024. [PMID: 38744531 PMCID: PMC11211721 DOI: 10.1523/jneurosci.2199-23.2024] [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: 11/20/2023] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that the model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting the network output and typically producing antinociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.
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Affiliation(s)
- Evan R Rogers
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109
| | - Marco Capogrosso
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Scott F Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan 48109
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3
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Mellbin A, Rongala U, Jörntell H, Bengtsson F. ECoG activity distribution patterns detects global cortical responses following weak tactile inputs. iScience 2024; 27:109338. [PMID: 38495818 PMCID: PMC10940986 DOI: 10.1016/j.isci.2024.109338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/30/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024] Open
Abstract
Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving patterns of the activity distributions using principal component analysis (PCA). We found that the localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal activity distribution patterns were detectable across a small number of ECoG channels, even when excluding the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human electroencephalography (EEG) for detection of pathological changes.
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Affiliation(s)
- Astrid Mellbin
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Biomedical Centre, Lund University, SE-223 62 Lund, Sweden
| | - Udaya Rongala
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Biomedical Centre, Lund University, SE-223 62 Lund, Sweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Biomedical Centre, Lund University, SE-223 62 Lund, Sweden
| | - Fredrik Bengtsson
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Biomedical Centre, Lund University, SE-223 62 Lund, Sweden
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4
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Etemadi L, Enander JM, Jörntell H. Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons. iScience 2023; 26:106885. [PMID: 37260754 PMCID: PMC10227419 DOI: 10.1016/j.isci.2023.106885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/13/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons in vivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern. To manipulate the internal cortical state, we here used brief electrical stimulation of the output region of the hippocampus, which preceded the delivery of specific tactile afferent input patterns to digit 2 of the anesthetized rat. We find that hippocampal output had a diversified, remarkably strong impact on the intracellular response types displayed by each neuron in the primary SI to each given tactile input pattern. Qualitatively, this impact was comparable to that previously described for cortical output, which was surprising given the widely assumed specific roles of the hippocampus, such as in cortical memory formation. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs and hence influence perception, potentially with affective and semantic components.
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Affiliation(s)
- Leila Etemadi
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Jonas M.D. Enander
- Center for Social and Affective Neuroscience, Linköping University, Linköping, Sweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
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5
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Nawaz A, Merces L, Ferro LMM, Sonar P, Bufon CCB. Impact of Planar and Vertical Organic Field-Effect Transistors on Flexible Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204804. [PMID: 36124375 DOI: 10.1002/adma.202204804] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/13/2022] [Indexed: 06/15/2023]
Abstract
The development of flexible and conformable devices, whose performance can be maintained while being continuously deformed, provides a significant step toward the realization of next-generation wearable and e-textile applications. Organic field-effect transistors (OFETs) are particularly interesting for flexible and lightweight products, because of their low-temperature solution processability, and the mechanical flexibility of organic materials that endows OFETs the natural compatibility with plastic and biodegradable substrates. Here, an in-depth review of two competing flexible OFET technologies, planar and vertical OFETs (POFETs and VOFETs, respectively) is provided. The electrical, mechanical, and physical properties of POFETs and VOFETs are critically discussed, with a focus on four pivotal applications (integrated logic circuits, light-emitting devices, memories, and sensors). It is pointed out that the flexible function of the relatively newer VOFET technology, along with its perspective on advancing the applicability of flexible POFETs, has not been reviewed so far, and the direct comparison regarding the performance of POFET- and VOFET-based flexible applications is most likely absent. With discussions spanning printed and wearable electronics, materials science, biotechnology, and environmental monitoring, this contribution is a clear stimulus to researchers working in these fields to engage toward the plentiful possibilities that POFETs and VOFETs offer to flexible electronics.
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Affiliation(s)
- Ali Nawaz
- Center for Sensors and Devices, Bruno Kessler Foundation (FBK), Trento, 38123, Italy
| | - Leandro Merces
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, 09126, Chemnitz, Germany
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, 13083-100, Brazil
| | - Letícia M M Ferro
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, 09126, Chemnitz, Germany
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, 13083-100, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, 13083-970, Brazil
| | - Prashant Sonar
- School of Chemistry and Physics, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
- Centre for Materials Science, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia
| | - Carlos C B Bufon
- MackGraphe - Graphene and Nanomaterials Research Center, Mackenzie Presbyterian Institute, São Paulo, 01302-907, Brazil
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6
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Li N, Wang Q, He C, Li J, Li X, Shen C, Huang B, Tang J, Yu H, Wang S, Du L, Yang W, Yang R, Shi D, Zhang G. 2D Semiconductor Based Flexible Photoresponsive Ring Oscillators for Artificial Vision Pixels. ACS NANO 2023; 17:991-999. [PMID: 36607196 DOI: 10.1021/acsnano.2c06921] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Artificial retina implantation provides an effective and feasible attempt for vision recovery in addition to retinal transplantation. The most advanced artificial retinas ever developed based on silicon technology are rigid and thus less compatible with the biosystem. Here we demonstrate flexible photoresponsive ring oscillators (PROs) based on the 2D semiconductor MoS2 for artificial retinas. Under natural light illuminations, arrayed PROs on flexible substrates serving as vision pixels can efficiently output light-intensity-dependent electrical pulses that are processable and transmittable in the human visual nerve system. Such PROs can work under low supply voltages below 1 V with a record-low power consumption, e.g. only 12.4 nW at a light intensity of 10 mW/cm2, decreased by ∼500 times compared with that of the state-of-the-art silicon devices. Such flexible artificial retinas with a simple device structure, high light-to-signal conversion efficiency, ultralow power consumption, and high tunability provide an alternative prosthesis for further clinical trials.
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Affiliation(s)
- Na Li
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Qinqin Wang
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Congli He
- Institute of Advanced Materials, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Jiawei Li
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Xiuzhen Li
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Cheng Shen
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Biying Huang
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Jian Tang
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Hua Yu
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Shuopei Wang
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Luojun Du
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Wei Yang
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Rong Yang
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Dongxia Shi
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Guangyu Zhang
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
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7
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Turecek J, Lehnert BP, Ginty DD. The encoding of touch by somatotopically aligned dorsal column subdivisions. Nature 2022; 612:310-315. [PMID: 36418401 PMCID: PMC9729103 DOI: 10.1038/s41586-022-05470-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/20/2022] [Indexed: 11/25/2022]
Abstract
The somatosensory system decodes a range of tactile stimuli to generate a coherent sense of touch. Discriminative touch of the body depends on signals conveyed from peripheral mechanoreceptors to the brain through the spinal cord dorsal column and its brainstem target, the dorsal column nuclei (DCN)1,2. Models of somatosensation emphasize that fast-conducting low-threshold mechanoreceptors (LTMRs) innervating the skin drive the DCN3,4. However, postsynaptic dorsal column (PSDC) neurons within the spinal cord dorsal horn also collect mechanoreceptor signals and form a second major input to the DCN5-7. The significance of PSDC neurons and their contributions to the coding of touch have remained unclear since their discovery. Here we show that direct LTMR input to the DCN conveys vibrotactile stimuli with high temporal precision. Conversely, PSDC neurons primarily encode touch onset and the intensity of sustained contact into the high-force range. LTMR and PSDC signals topographically realign in the DCN to preserve precise spatial detail. Different DCN neuron subtypes have specialized responses that are generated by distinct combinations of LTMR and PSDC inputs. Thus, LTMR and PSDC subdivisions of the dorsal column encode different tactile features and differentially converge in the DCN to generate specific ascending sensory processing streams.
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Affiliation(s)
- Josef Turecek
- Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Brendan P Lehnert
- Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - David D Ginty
- Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA.
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8
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Go GT, Lee Y, Seo DG, Lee TW. Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201864. [PMID: 35925610 DOI: 10.1002/adma.202201864] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 07/17/2022] [Indexed: 06/15/2023]
Abstract
Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.
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Affiliation(s)
- Gyeong-Tak Go
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Yeongjun Lee
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Soft Foundry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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9
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Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons. iScience 2022; 25:104083. [PMID: 35372805 PMCID: PMC8971951 DOI: 10.1016/j.isci.2022.104083] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/14/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022] Open
Abstract
The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.
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10
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Etemadi L, Enander JMD, Jörntell H. Remote cortical perturbation dynamically changes the network solutions to given tactile inputs in neocortical neurons. iScience 2022; 25:103557. [PMID: 34977509 PMCID: PMC8689199 DOI: 10.1016/j.isci.2021.103557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/18/2021] [Accepted: 12/01/2021] [Indexed: 11/17/2022] Open
Abstract
The neocortex has a globally encompassing network structure, which for each given input constrains the possible combinations of neuronal activations across it. Hence, its network contains solutions. But in addition, the cortex has an ever-changing multidimensional internal state, causing each given input to result in a wide range of specific neuronal activations. Here we use intracellular recordings in somatosensory cortex (SI) neurons of anesthetized rats to show that remote, subthreshold intracortical electrical perturbation can impact such constraints on the responses to a set of spatiotemporal tactile input patterns. Whereas each given input pattern normally induces a wide set of preferred response states, when combined with cortical perturbation response states that did not otherwise occur were induced and consequently made other response states less likely. The findings indicate that the physiological network structure can dynamically change as the state of any given cortical region changes, thereby enabling a rich, multifactorial, perceptual capability. Tactile sensory input patterns evoke multi-structure cortical neuron responses Multi-structure responses are shown to be impacted by remote cortical regions Highly dynamic neuron responses reflects global cortical information integration Perception hence depends on globally distributed activity at the time of input
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Affiliation(s)
- Leila Etemadi
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, BMC F10 Tornavägen 10, 221 84 Lund, Sweden
| | - Jonas M D Enander
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, BMC F10 Tornavägen 10, 221 84 Lund, Sweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, BMC F10 Tornavägen 10, 221 84 Lund, Sweden
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11
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Conner JM, Bohannon A, Igarashi M, Taniguchi J, Baltar N, Azim E. Modulation of tactile feedback for the execution of dexterous movement. Science 2021; 374:316-323. [PMID: 34648327 DOI: 10.1126/science.abh1123] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- James M Conner
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Andrew Bohannon
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Masakazu Igarashi
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - James Taniguchi
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nicholas Baltar
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
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12
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Santos-Mayo A, Moratti S, de Echegaray J, Susi G. A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity. BIOLOGY 2021; 10:biology10080801. [PMID: 34440033 PMCID: PMC8389551 DOI: 10.3390/biology10080801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/12/2021] [Accepted: 08/16/2021] [Indexed: 12/22/2022]
Abstract
Simple Summary A computational model of primates’ early visual processing, showing orientation selectivity, is presented. The system importantly integrates two key elements: (1) a neuromorphic spike-decoding structure that considerably resembles the circuitry between layers IV and II/III of the primary visual cortex, both in topology and operation; (2) the plasticity of intrinsic excitability, to embed recent findings about the operation of the same area. The model is proposed as a tool for the analysis and reproduction of the orientation selectivity phenomenon, whose underlying neuronal-level computational mechanisms are today the subject of intense scrutiny. In response to rotated Gabor patches the model is able to exhibit realistic orientation tuning curves and to reproduce responses similar to those found in neurophysiological recordings from the primary visual cortex obtained under the same task, considering different stages of the network. This demonstrates its aptness to capture the mechanisms underlying the evoked response in the primary visual cortex. Our tool is available online, and can be expanded to other experiments using a dedicated software library developed by the authors, to elucidate the computational mechanisms underlying orientation selectivity. Abstract Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity.
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Affiliation(s)
- Alejandro Santos-Mayo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain; (A.S.-M.); (S.M.); (J.d.E.)
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid, 28040 Madrid, Spain
| | - Stephan Moratti
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain; (A.S.-M.); (S.M.); (J.d.E.)
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid, 28040 Madrid, Spain
- Laboratory of Clinical Neuroscience, Center for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain
| | - Javier de Echegaray
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain; (A.S.-M.); (S.M.); (J.d.E.)
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid, 28040 Madrid, Spain
| | - Gianluca Susi
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain; (A.S.-M.); (S.M.); (J.d.E.)
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid, 28040 Madrid, Spain
- Department of Civil Engineering and Computer Science, University of Rome “Tor Vergata”, 00133 Rome, Italy
- Correspondence: ; Tel.: +34-(61)-86893399-79317
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13
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Versteeg C, Rosenow JM, Bensmaia SJ, Miller LE. Encoding of limb state by single neurons in the cuneate nucleus of awake monkeys. J Neurophysiol 2021; 126:693-706. [PMID: 34010577 PMCID: PMC8409958 DOI: 10.1152/jn.00568.2020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/13/2021] [Accepted: 05/13/2021] [Indexed: 12/24/2022] Open
Abstract
The cuneate nucleus (CN) is among the first sites along the neuraxis where proprioceptive signals can be integrated, transformed, and modulated. The objective of the study was to characterize the proprioceptive representations in CN. To this end, we recorded from single CN neurons in three monkeys during active reaching and passive limb perturbation. We found that many neurons exhibited responses that were tuned approximately sinusoidally to limb movement direction, as has been found for other sensorimotor neurons. The distribution of their preferred directions (PDs) was highly nonuniform and resembled that of muscle spindles within individual muscles, suggesting that CN neurons typically receive inputs from only a single muscle. We also found that the responses of proprioceptive CN neurons tended to be modestly amplified during active reaching movements compared to passive limb perturbations, in contrast to cutaneous CN neurons whose responses were not systematically different in the active and passive conditions. Somatosensory signals thus seem to be subject to a "spotlighting" of relevant sensory information rather than uniform suppression as has been suggested previously.NEW & NOTEWORTHY The cuneate nucleus (CN) is the somatosensory gateway into the brain, and only recently has it been possible to record these signals from an awake animal. We recorded single CN neurons in monkeys. Proprioceptive CN neurons appear to receive input from very few muscles, and their sensitivity to movement changes reliably during reaching relative to passive arm perturbations. Sensitivity is generally increased, but not exclusively so, as though CN "spotlights" critical proprioceptive information during reaching.
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Affiliation(s)
- Christopher Versteeg
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois
| | - Joshua M Rosenow
- Department of Neurology, Northwestern University, Chicago, Illinois
- Department of Neurological Surgery, Northwestern University, Chicago, Illinois
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute of Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois
- Shirley Ryan AbilityLab, Chicago, Illinois
- Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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14
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Norrlid J, Enander JMD, Mogensen H, Jörntell H. Multi-structure Cortical States Deduced From Intracellular Representations of Fixed Tactile Input Patterns. Front Cell Neurosci 2021; 15:677568. [PMID: 34194301 PMCID: PMC8236821 DOI: 10.3389/fncel.2021.677568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain has a never-ending internal activity, whose spatiotemporal evolution interacts with external inputs to constrain their impact on brain activity and thereby how we perceive them. We used reproducible touch-related spatiotemporal sensory inputs and recorded intracellularly from rat (Sprague-Dawley, male) neocortical neurons to characterize this interaction. The synaptic responses, or the summed input of the networks connected to the neuron, varied greatly to repeated presentations of the same tactile input pattern delivered to the tip of digit 2. Surprisingly, however, these responses tended to sort into a set of specific time-evolving response types, unique for each neuron. Further, using a set of eight such tactile input patterns, we found each neuron to exhibit a set of specific response types for each input provided. Response types were not determined by the global cortical state, but instead likely depended on the time-varying state of the specific subnetworks connected to each neuron. The fact that some types of responses recurred indicates that the cortical network had a non-continuous landscape of solutions for these tactile inputs. Therefore, our data suggest that sensory inputs combine with the internal dynamics of the brain networks, thereby causing them to fall into one of the multiple possible perceptual attractor states. The neuron-specific instantiations of response types we observed suggest that the subnetworks connected to each neuron represent different components of those attractor states. Our results indicate that the impact of cortical internal states on external inputs is substantially more richly resolvable than previously shown.
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Affiliation(s)
- Johanna Norrlid
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Jonas M D Enander
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Hannes Mogensen
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
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15
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Versteeg C, Chowdhury RH, Miller LE. Cuneate nucleus: The somatosensory gateway to the brain. CURRENT OPINION IN PHYSIOLOGY 2021; 20:206-215. [PMID: 33869911 DOI: 10.1016/j.cophys.2021.02.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Much remains unknown about the transformation of proprioceptive afferent input from the periphery to the cortex. Until recently, the only recordings from neurons in the cuneate nucleus (CN) were from anesthetized animals. We are beginning to learn more about how the sense of proprioception is transformed as it propagates centrally. Recent recordings from microelectrode arrays chronically implanted in CN have revealed that CN neurons with muscle-like properties have a greater sensitivity to active reaching movements than to passive limb displacement, and we find that these neurons have receptive fields that resemble single muscles. In this review, we focus on the varied uses of proprioceptive input and the possible role of CN in processing this information.
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Affiliation(s)
- Christopher Versteeg
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern 7 University, Evanston, IL, USA
| | - Raeed H Chowdhury
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 10 Pittsburgh, PA, USA
| | - Lee E Miller
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern 7 University, Evanston, IL, USA.,Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, 13 IL, USA.,Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, 16 Northwestern University, Chicago, IL, USA.,Shirley Ryan AbilityLab, Chicago, IL, USA
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16
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Parvizi-Fard A, Amiri M, Kumar D, Iskarous MM, Thakor NV. A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Sci Rep 2021; 11:1320. [PMID: 33446742 PMCID: PMC7809061 DOI: 10.1038/s41598-020-80132-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/17/2020] [Indexed: 01/24/2023] Open
Abstract
To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications.
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Affiliation(s)
- Adel Parvizi-Fard
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
| | - Deepesh Kumar
- SINAPSE Laboratory, National University of Singapore, Singapore, Singapore
| | - Mark M Iskarous
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish V Thakor
- SINAPSE Laboratory, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
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17
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Torricelli F, Adrahtas DZ, Bao Z, Berggren M, Biscarini F, Bonfiglio A, Bortolotti CA, Frisbie CD, Macchia E, Malliaras GG, McCulloch I, Moser M, Nguyen TQ, Owens RM, Salleo A, Spanu A, Torsi L. Electrolyte-gated transistors for enhanced performance bioelectronics. NATURE REVIEWS. METHODS PRIMERS 2021; 1. [PMID: 35475166 DOI: 10.1038/s43586-021-00065-8] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals and stably operating in aqueous environments, have emerged as fundamental building blocks in bioelectronics. In this Primer, the different EGT architectures are described with the fundamental mechanisms underpinning their functional operation, providing insight into key experiments including necessary data analysis and validation. Several organic and inorganic materials used in the EGT structures and the different fabrication approaches for an optimal experimental design are presented and compared. The functional bio-layers and/or biosystems integrated into or interfaced to EGTs, including self-organization and self-assembly strategies, are reviewed. Relevant and promising applications are discussed, including two-dimensional and three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic and neuromorphic bio-interfaces, prosthetics and robotics. Advantages, limitations and possible optimizations are also surveyed. Finally, current issues and future directions for further developments and applications are discussed.
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Affiliation(s)
- Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Demetra Z Adrahtas
- Department of Chemical Engineering & Materials Science, University of Minnesota, Minneapolis, MN, USA
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Fabio Biscarini
- Dipartimento di Scienze della Vita, Università degli Studi di Modena e Reggio Emilia, Modena, Italy.,Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
| | - Annalisa Bonfiglio
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Carlo A Bortolotti
- Dipartimento di Scienze della Vita, Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | - C Daniel Frisbie
- Department of Chemical Engineering & Materials Science, University of Minnesota, Minneapolis, MN, USA
| | - Eleonora Macchia
- Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Iain McCulloch
- Physical Sciences and Engineering Division, KAUST Solar Center (KSC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.,Department of Chemistry, Chemistry Research Laboratory, University of Oxford, Oxford, UK
| | - Maximilian Moser
- Department of Chemistry, Chemistry Research Laboratory, University of Oxford, Oxford, UK
| | - Thuc-Quyen Nguyen
- Department of Chemistry & Biochemistry, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Róisín M Owens
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Andrea Spanu
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Luisa Torsi
- Department of Chemistry, University of Bari 'Aldo Moro', Bari, Italy
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18
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Hay E, Pruszynski JA. Orientation processing by synaptic integration across first-order tactile neurons. PLoS Comput Biol 2020; 16:e1008303. [PMID: 33264287 PMCID: PMC7710081 DOI: 10.1371/journal.pcbi.1008303] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 09/03/2020] [Indexed: 01/21/2023] Open
Abstract
Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.
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Affiliation(s)
- Etay Hay
- Department of Physiology and Pharmacology, Western University, London, Canada
- Brain and Mind Institute, Western University, London, Canada
- Robarts Research Institute, Western University, London, Canada
| | - J. Andrew Pruszynski
- Department of Physiology and Pharmacology, Western University, London, Canada
- Brain and Mind Institute, Western University, London, Canada
- Robarts Research Institute, Western University, London, Canada
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19
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Biological data questions the support of the self inhibition required for pattern generation in the half center model. PLoS One 2020; 15:e0238586. [PMID: 32915814 PMCID: PMC7485810 DOI: 10.1371/journal.pone.0238586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 08/19/2020] [Indexed: 11/19/2022] Open
Abstract
Locomotion control in mammals has been hypothesized to be governed by a central pattern generator (CPG) located in the circuitry of the spinal cord. The most common model of the CPG is the half center model, where two pools of neurons generate alternating, oscillatory activity. In this model, the pools reciprocally inhibit each other ensuring alternating activity. There is experimental support for reciprocal inhibition. However another crucial part of the half center model is a self inhibitory mechanism which prevents the neurons of each individual pool from infinite firing. Self-inhibition is hence necessary to obtain alternating activity. But critical parts of the experimental bases for the proposed mechanisms for self-inhibition were obtained in vitro, in preparations of juvenile animals. The commonly used adaptation of spike firing does not appear to be present in adult animals in vivo. We therefore modeled several possible self inhibitory mechanisms for locomotor control. Based on currently published data, previously proposed hypotheses of the self inhibitory mechanism, necessary to support the CPG hypothesis, seems to be put into question by functional evaluation tests or by in vivo data. This opens for alternative explanations of how locomotion activity patterns in the adult mammal could be generated.
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20
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Loutit AJ, Vickery RM, Potas JR. Functional organization and connectivity of the dorsal column nuclei complex reveals a sensorimotor integration and distribution hub. J Comp Neurol 2020; 529:187-220. [PMID: 32374027 DOI: 10.1002/cne.24942] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 12/12/2022]
Abstract
The dorsal column nuclei complex (DCN-complex) includes the dorsal column nuclei (DCN, referring to the gracile and cuneate nuclei collectively), external cuneate, X, and Z nuclei, and the median accessory nucleus. The DCN are organized by both somatotopy and modality, and have a diverse range of afferent inputs and projection targets. The functional organization and connectivity of the DCN implicate them in a variety of sensorimotor functions, beyond their commonly accepted role in processing and transmitting somatosensory information to the thalamus, yet this is largely underappreciated in the literature. To consolidate insights into their sensorimotor functions, this review examines the morphology, organization, and connectivity of the DCN and their associated nuclei. First, we briefly discuss the receptors, afferent fibers, and pathways involved in conveying tactile and proprioceptive information to the DCN. Next, we review the modality and somatotopic arrangements of the remaining constituents of the DCN-complex. Finally, we examine and discuss the functional implications of the myriad of DCN-complex projection targets throughout the diencephalon, midbrain, and hindbrain, in addition to their modulatory inputs from the cortex. The organization and connectivity of the DCN-complex suggest that these nuclei should be considered a complex integration and distribution hub for sensorimotor information.
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Affiliation(s)
- Alastair J Loutit
- School of Medical Sciences, UNSW Sydney, Sydney, New South Wales, Australia.,The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Richard M Vickery
- School of Medical Sciences, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jason R Potas
- School of Medical Sciences, UNSW Sydney, Sydney, New South Wales, Australia.,The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
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21
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Kheradpisheh SR, Masquelier T. Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron. Int J Neural Syst 2020; 30:2050027. [DOI: 10.1142/s0129065720500276] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, named S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximated error gradients can be computed backward in a feedforward network with any number of layers. This approach reaches state-of-the-art performance with supervised multi-fully connected layer SNNs: test accuracy of 97.4% for the MNIST dataset, and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we use, nonleaky integrate-and-fire, is much simpler than the one used in all previous works. The source codes of the proposed S4NN are publicly available at https://github.com/SRKH/S4NN .
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Affiliation(s)
- Saeed Reza Kheradpisheh
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
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22
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Shao Y, Hayward V, Visell Y. Compression of dynamic tactile information in the human hand. SCIENCE ADVANCES 2020; 6:eaaz1158. [PMID: 32494610 PMCID: PMC7159916 DOI: 10.1126/sciadv.aaz1158] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/17/2020] [Indexed: 05/16/2023]
Abstract
A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.
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Affiliation(s)
- Yitian Shao
- Department of Electrical and Computer Engineering, Media Arts and Technology Program, Department of Mechanical Engineering, and California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Vincent Hayward
- Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, F-75005 Paris, France
- Centre for the Study of the Senses, School of Advanced Study, University of London, London, UK
- Actronika SAS, Paris, France
| | - Yon Visell
- Department of Electrical and Computer Engineering, Media Arts and Technology Program, Department of Mechanical Engineering, and California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
- Corresponding author.
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23
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Park HL, Lee Y, Kim N, Seo DG, Go GT, Lee TW. Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1903558. [PMID: 31559670 DOI: 10.1002/adma.201903558] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 07/10/2019] [Indexed: 05/08/2023]
Abstract
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
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Affiliation(s)
- Hea-Lim Park
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Yeongjun Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- BK21 PLUS SNU Materials Division for Educating Creative Global Leaders, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Naryung Kim
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Gyeong-Tak Go
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- BK21 PLUS SNU Materials Division for Educating Creative Global Leaders, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- Institute of Engineering Research Research Institute of Advanced Materials, Nano Systems Institute (NSI), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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24
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Rongala UB, Mazzoni A, Spanne A, Jörntell H, Oddo CM. Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions. Neural Netw 2020; 123:273-287. [PMID: 31887687 DOI: 10.1016/j.neunet.2019.11.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 10/22/2019] [Accepted: 11/25/2019] [Indexed: 11/18/2022]
Abstract
We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications.
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Affiliation(s)
- Udaya B Rongala
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, 56127 Pisa, Italy; Department of Linguistics and Comparative Cultural Studies, Ca' Foscari University of Venice, 30123 Venice, Italy; Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
| | - Alberto Mazzoni
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, 56127 Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Anton Spanne
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden
| | - Calogero M Oddo
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, 56127 Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
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25
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Loutit AJ, Potas JR. Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex. Front Neurosci 2020; 14:156. [PMID: 32184706 PMCID: PMC7058659 DOI: 10.3389/fnins.2020.00156] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/10/2020] [Indexed: 12/12/2022] Open
Abstract
Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.
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Affiliation(s)
| | - Jason R. Potas
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
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26
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Strauss I, Valle G, Artoni F, D'Anna E, Granata G, Di Iorio R, Guiraud D, Stieglitz T, Rossini PM, Raspopovic S, Petrini FM, Micera S. Characterization of multi-channel intraneural stimulation in transradial amputees. Sci Rep 2019; 9:19258. [PMID: 31848384 PMCID: PMC6917705 DOI: 10.1038/s41598-019-55591-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 11/20/2019] [Indexed: 11/29/2022] Open
Abstract
Although peripheral nerve stimulation using intraneural electrodes has been shown to be an effective and reliable solution to restore sensory feedback after hand loss, there have been no reports on the characterization of multi-channel stimulation. A deeper understanding of how the simultaneous stimulation of multiple electrode channels affects the evoked sensations should help in improving the definition of encoding strategies for bidirectional prostheses. We characterized the sensations evoked by simultaneous stimulation of median and ulnar nerves (multi-channel configuration) in four transradial amputees who had been implanted with four TIMEs (Transverse Intrafascicular Multichannel Electrodes). The results were compared with the characterization of single-channel stimulation. The sensations were characterized in terms of location, extent, type, and intensity. Combining two or more single-channel configurations caused a linear combination of the sensation locations and types perceived with such single-channel stimulations. Interestingly, this was also true when two active sites from the same nerve were stimulated. When stimulating in multi-channel configuration, the charge needed from each electrode channel to evoke a sensation was significantly lower than the one needed in single-channel configuration (sensory facilitation). This result was also supported by electroencephalography (EEG) recordings during nerve stimulation. Somatosensory potentials evoked by multi-channel stimulation confirmed that sensations in the amputated hand were perceived by the subjects and that a perceptual sensory facilitation occurred. Our results should help the future development of more efficient bidirectional prostheses by providing guidelines for the development of more complex stimulation approaches to effectively restore multiple sensations at the same time.
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Affiliation(s)
- I Strauss
- Center for Neuroscience, Neurotechnology, and Bioelectronic Medicine and The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - G Valle
- Center for Neuroscience, Neurotechnology, and Bioelectronic Medicine and The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - F Artoni
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - E D'Anna
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - G Granata
- Fondazione Policlinico Agostino Gemelli-IRCCS, Roma, Italy
| | - R Di Iorio
- Institute of Neurology, Catholic University of The Sacred Heart, Policlinic A. Gemelli Foundation, Roma, Italy
| | - D Guiraud
- University of Montpellier, INRIA, CAMIN team, 860 Rue St Priest, 34090, Montpellier, France
| | - T Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, D-79110, Germany
| | - P M Rossini
- Fondazione Policlinico Agostino Gemelli-IRCCS, Roma, Italy
- Institute of Neurology, Catholic University of The Sacred Heart, Policlinic A. Gemelli Foundation, Roma, Italy
| | - S Raspopovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich (ETH), Zürich, 8092, Switzerland
| | - F M Petrini
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich (ETH), Zürich, 8092, Switzerland.
| | - S Micera
- Center for Neuroscience, Neurotechnology, and Bioelectronic Medicine and The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
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27
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Lieber JD, Bensmaia SJ. Emergence of an Invariant Representation of Texture in Primate Somatosensory Cortex. Cereb Cortex 2019; 30:3228-3239. [PMID: 31813989 PMCID: PMC7197205 DOI: 10.1093/cercor/bhz305] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 11/08/2019] [Accepted: 11/12/2019] [Indexed: 01/13/2023] Open
Abstract
A major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons (from Brodmann areas 3b, 1, and 2). We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.
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Affiliation(s)
- Justin D Lieber
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Sliman J Bensmaia
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, 60637, USA.,Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, 60637, USA
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28
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Wahlbom A, Enander JMD, Bengtsson F, Jörntell H. Focal neocortical lesions impair distant neuronal information processing. J Physiol 2019; 597:4357-4371. [PMID: 31342538 PMCID: PMC6852703 DOI: 10.1113/jp277717] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 07/08/2019] [Indexed: 12/14/2022] Open
Abstract
KEY POINTS Parts of the fields of neuroscience and neurology consider the neocortex to be a functionally parcelled structure. Viewed through such a conceptual filter, there are multiple clinical observations after localized stroke lesions that seem paradoxical. We tested the effect that localized stroke-like lesions have on neuronal information processing in a part of the neocortex that is distant to the lesion using animal experiments. We find that the distant lesion degrades the quality of neuronal information processing of tactile input patterns in primary somatosensory cortex. The findings suggest that even the processing of primary sensory information depends on an intact neocortical network, with the implication that all neocortical processing may rely on widespread interactions across large parts of the cortex. ABSTRACT Recent clinical studies report a surprisingly weak relationship between the location of cortical brain lesions and the resulting functional deficits. From a neuroscience point of view, such findings raise questions as to what extent functional localization applies in the neocortex and to what extent the functions of different regions depend on the integrity of others. Here we provide an in-depth analysis of the changes in the function of the neocortical neuronal networks after distant focal stroke-like lesions in the anaesthetized rat. Using a recently introduced high resolution analysis of neuronal information processing, consisting of pre-set spatiotemporal patterns of tactile afferent activation against which the neuronal decoding performance can be quantified, we found that stroke-like lesions in distant parts of the cortex significantly degraded the decoding performance of individual neocortical neurons in the primary somatosensory cortex (decoding performance decreased from 30.9% to 24.2% for n = 22 neurons, Wilcoxon signed rank test, P = 0.028). This degrading effect was not due to changes in the firing frequency of the neuron (Wilcoxon signed rank test, P = 0.499) and was stronger the higher the decoding performance of the neuron, indicating a specific impact on the information processing capacity in the cortex. These findings suggest that even primary sensory processing depends on widely distributed cortical networks and could explain observations of focal stroke lesions affecting a large range of functions.
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Affiliation(s)
- Anders Wahlbom
- Neural Basis of Sensorimotor ControlDepartment of Experimental Medical ScienceBMC F10 Tornavägen 10SE‐221 84LundSweden
| | - Jonas M. D. Enander
- Neural Basis of Sensorimotor ControlDepartment of Experimental Medical ScienceBMC F10 Tornavägen 10SE‐221 84LundSweden
| | - Fredrik Bengtsson
- Neural Basis of Sensorimotor ControlDepartment of Experimental Medical ScienceBMC F10 Tornavägen 10SE‐221 84LundSweden
| | - Henrik Jörntell
- Neural Basis of Sensorimotor ControlDepartment of Experimental Medical ScienceBMC F10 Tornavägen 10SE‐221 84LundSweden
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29
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Loutit AJ, Shivdasani MN, Maddess T, Redmond SJ, Morley JW, Stuart GJ, Birznieks I, Vickery RM, Potas JR. Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei. Front Syst Neurosci 2019; 13:11. [PMID: 30983977 PMCID: PMC6448039 DOI: 10.3389/fnsys.2019.00011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 02/26/2019] [Indexed: 02/04/2023] Open
Abstract
The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.
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Affiliation(s)
- Alastair J Loutit
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.,Eccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Mohit N Shivdasani
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.,Bionics Institute, East Melbourne, VIC, Australia
| | - Ted Maddess
- Eccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Stephen J Redmond
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.,UCD School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.,UCD Centre for Biomedical Engineering, University College Dublin, Dublin, Ireland
| | - John W Morley
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.,School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | - Greg J Stuart
- Eccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | | | | | - Jason R Potas
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.,Eccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
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30
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Enander JMD, Spanne A, Mazzoni A, Bengtsson F, Oddo CM, Jörntell H. Ubiquitous Neocortical Decoding of Tactile Input Patterns. Front Cell Neurosci 2019; 13:140. [PMID: 31031596 PMCID: PMC6474209 DOI: 10.3389/fncel.2019.00140] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/20/2019] [Indexed: 11/13/2022] Open
Abstract
Whereas functional localization historically has been a key concept in neuroscience, direct neuronal recordings show that input of a particular modality can be recorded well outside its primary receiving areas in the neocortex. Here, we wanted to explore if such spatially unbounded inputs potentially contain any information about the quality of the input received. We utilized a recently introduced approach to study the neuronal decoding capacity at a high resolution by delivering a set of electrical, highly reproducible spatiotemporal tactile afferent activation patterns to the skin of the contralateral second digit of the forepaw of the anesthetized rat. Surprisingly, we found that neurons in all areas recorded from, across all cortical depths tested, could decode the tactile input patterns, including neurons of the primary visual cortex. Within both somatosensory and visual cortical areas, the combined decoding accuracy of a population of neurons was higher than for the best performing single neuron within the respective area. Such cooperative decoding indicates that not only did individual neurons decode the input, they also did so by generating responses with different temporal profiles compared to other neurons, which suggests that each neuron could have unique contributions to the tactile information processing. These findings suggest that tactile processing in principle could be globally distributed in the neocortex, possibly for comparison with internal expectations and disambiguation processes relying on other modalities.
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Affiliation(s)
- Jonas M. D. Enander
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Anton Spanne
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Fredrik Bengtsson
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | | | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
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31
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Enander JM, Jörntell H. Somatosensory Cortical Neurons Decode Tactile Input Patterns and Location from Both Dominant and Non-dominant Digits. Cell Rep 2019; 26:3551-3560.e4. [DOI: 10.1016/j.celrep.2019.02.099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 12/10/2018] [Accepted: 02/22/2019] [Indexed: 10/27/2022] Open
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32
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Delhaye BP, Long KH, Bensmaia SJ. Neural Basis of Touch and Proprioception in Primate Cortex. Compr Physiol 2018; 8:1575-1602. [PMID: 30215864 PMCID: PMC6330897 DOI: 10.1002/cphy.c170033] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The sense of proprioception allows us to keep track of our limb posture and movements and the sense of touch provides us with information about objects with which we come into contact. In both senses, mechanoreceptors convert the deformation of tissues-skin, muscles, tendons, ligaments, or joints-into neural signals. Tactile and proprioceptive signals are then relayed by the peripheral nerves to the central nervous system, where they are processed to give rise to percepts of objects and of the state of our body. In this review, we first examine briefly the receptors that mediate touch and proprioception, their associated nerve fibers, and pathways they follow to the cerebral cortex. We then provide an overview of the different cortical areas that process tactile and proprioceptive information. Next, we discuss how various features of objects-their shape, motion, and texture, for example-are encoded in the various cortical fields, and the susceptibility of these neural codes to attention and other forms of higher-order modulation. Finally, we summarize recent efforts to restore the senses of touch and proprioception by electrically stimulating somatosensory cortex. © 2018 American Physiological Society. Compr Physiol 8:1575-1602, 2018.
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Affiliation(s)
- Benoit P Delhaye
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, USA
| | - Katie H Long
- Committee on Computational Neuroscience, University of Chicago, Chicago, USA
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, USA.,Committee on Computational Neuroscience, University of Chicago, Chicago, USA
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33
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Rongala UB, Spanne A, Mazzoni A, Bengtsson F, Oddo CM, Jörntell H. Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations. Front Cell Neurosci 2018; 12:210. [PMID: 30108485 PMCID: PMC6079306 DOI: 10.3389/fncel.2018.00210] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 06/26/2018] [Indexed: 12/12/2022] Open
Abstract
How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.
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Affiliation(s)
- Udaya B Rongala
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Anton Spanne
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fredrik Bengtsson
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
| | - Calogero M Oddo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Henrik Jörntell
- Section for Neurobiology, Department of Experimental Medical Sciences, Biomedical Center, Lund University, Lund, Sweden
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34
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Shishido SI, Toda T. Temporal Patterns of Individual Neuronal Firing in Rat Dorsal Column Nuclei Provide Information Required for Somatosensory Discrimination. TOHOKU J EXP MED 2018; 243:115-126. [PMID: 29070782 DOI: 10.1620/tjem.243.115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A wealth of mechanical information from the body generates various forms of sensory experience during touch or kinesthesia. Dorsal column nuclei (DCN) in the medulla are the first relay station for somatosensory inputs from peripheral receptors. These nuclei integrate somatosensory information and send the output to higher-order centers; therefore, investigating the firing patterns of DCN neurons should elucidate coding principles within the somatosensory system. In this study, we quantified the firing patterns of DCN neurons and examined whether the firing patterns of particular neurons are altered when moving tactile stimuli are applied in different directions. The activities of 17 neurons in the DCN of anesthetized rats were selected and their firing patterns were analyzed using LvR, which refers to the local variation of intervals of action potentials (i.e., the cross-correlation between consecutive intervals of action potentials) compensated by the refractoriness constant, R. The LvR of the 17 neurons ranged widely from 0.35 to 2.28. Of the 17 neurons, 12 responded to hair deflection (hair neurons), whereas five responded specifically to movement of forelimb joints. In 11 of 12 hair neurons, moving stimuli were applied in two to four different directions, which yielded 25 pairs of comparisons. Of these, 14 pairs (56%) showed significant differences in LvR. Among these 14 pairs, the range of LvR fluctuation was 0.13 ± 0.06 (mean ± standard deviation) and its effect size (Cohen's d) was 0.6 ± 0.2. These results suggest that the firing pattern of individual DCN neurons may contribute to somatosensory discrimination.
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Affiliation(s)
- Shin-Ichiro Shishido
- Division of Oral Physiology, Tohoku University Graduate School of Dentistry.,Akamon Oriental Medical College
| | - Takashi Toda
- Division of Oral Physiology, Tohoku University Graduate School of Dentistry
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35
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Kim Y, Chortos A, Xu W, Liu Y, Oh JY, Son D, Kang J, Foudeh AM, Zhu C, Lee Y, Niu S, Liu J, Pfattner R, Bao Z, Lee TW. A bioinspired flexible organic artificial afferent nerve. Science 2018; 360:998-1003. [DOI: 10.1126/science.aao0098] [Citation(s) in RCA: 683] [Impact Index Per Article: 113.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.
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36
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Parasuram H, Nair B, Naldi G, D'Angelo E, Diwakar S. Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials. Ann Neurosci 2017; 25:11-24. [PMID: 29887679 DOI: 10.1159/000481905] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 09/05/2017] [Indexed: 12/27/2022] Open
Abstract
Background The cerebellar granular layer input stage of cerebellum receives information from tactile and sensory regions of the body. The somatosensory activity in the cerebellar granular layer corresponds to sensory and tactile input has been observed by recording Local Field Potential (LFP) from the Crus-IIa regions of cerebellum in brain slices and in anesthetized animals. Purpose In this paper, a detailed biophysical model of Wistar rat cerebellum granular layer network model and LFP modelling schemas were used to simulate circuit's evoked response. Methods Point Source Approximation and Line Source Approximation were used to reconstruct the network LFP. The LFP mechanism in in vitro was validated in network model and generated the in vivo LFP using the same mechanism. Results The network simulations distinctly displayed the Trigeminal and Cortical (TC) wave components generated by 2 independent bursts implicating the generation of TC waves by 2 independent granule neuron populations. Induced plasticity was simulated to estimate granule neuron activation related population responses. As a prediction, cerebellar dysfunction (ataxia) was also studied using the model. Dysfunction at individual neurons in the network was affected by the population response. Conclusion Our present study utilizes available knowledge on known mechanisms in a single cell and associates network function to population responses.
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Affiliation(s)
- Harilal Parasuram
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University), Kollam, India
| | - Bipin Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University), Kollam, India
| | - Giovanni Naldi
- Department of Mathematics, University of Milan, Milan, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy
| | - Shyam Diwakar
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University), Kollam, India
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37
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Loutit AJ, Maddess T, Redmond SJ, Morley JW, Stuart GJ, Potas JR. Characterisation and functional mapping of surface potentials in the rat dorsal column nuclei. J Physiol 2017; 595:4507-4524. [PMID: 28333372 DOI: 10.1113/jp273759] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 03/10/2017] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS The brainstem dorsal column nuclei (DCN) process sensory information arising from the body before it reaches the brain and becomes conscious. Despite significant investigations into sensory coding in peripheral nerves and the somatosensory cortex, little is known about how sensory information arising from the periphery is represented in the DCN. Following stimulation of hind-limb nerves, we mapped and characterised the evoked electrical signatures across the DCN surface. We show that evoked responses recorded from the DCN surface are highly reproducible and are unique to nerves carrying specific sensory information. ABSTRACT The brainstem dorsal column nuclei (DCN) play a role in early processing of somatosensory information arising from a variety of functionally distinct peripheral structures, before being transmitted to the cortex via the thalamus. To improve our understanding of how sensory information is represented by the DCN, we characterised and mapped low- (<200 Hz) and high-frequency (550-3300 Hz) components of nerve-evoked DCN surface potentials. DCN surface potentials were evoked by electrical stimulation of the left and right nerves innervating cutaneous structures (sural nerve), or a mix of cutaneous and deep structures (peroneal nerve), in 8-week-old urethane-anaesthetised male Wistar rats. Peroneal nerve-evoked DCN responses demonstrated low-frequency events with significantly longer durations, more high-frequency events and larger magnitudes compared to responses evoked from sural nerve stimulation. Hotspots of low- and high-frequency DCN activity were found ipsilateral to stimulated nerves but were not symmetrically organised. In conclusion, we find that sensory inputs from peripheral nerves evoke unique and characteristic DCN activity patterns that are highly reproducible both within and across animals.
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Affiliation(s)
- Alastair J Loutit
- The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Ted Maddess
- The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
| | - Stephen J Redmond
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - John W Morley
- School of Medicine, Western Sydney University, Sydney, New South Wales, 2560, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Greg J Stuart
- The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
| | - Jason R Potas
- The Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia.,ANU Medical School, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
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Oddo CM, Mazzoni A, Spanne A, Enander JMD, Mogensen H, Bengtsson F, Camboni D, Micera S, Jörntell H. Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons. Sci Rep 2017; 8:45898. [PMID: 28374841 PMCID: PMC5379202 DOI: 10.1038/srep45898] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 03/02/2017] [Indexed: 11/24/2022] Open
Abstract
Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.
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Affiliation(s)
- Calogero M Oddo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Anton Spanne
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Jonas M D Enander
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Hannes Mogensen
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Fredrik Bengtsson
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Domenico Camboni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
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Richardson AG, Weigand PK, Sritharan SY, Lucas TH. A chronic neural interface to the macaque dorsal column nuclei. J Neurophysiol 2016; 115:2255-64. [PMID: 26912601 DOI: 10.1152/jn.01083.2015] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 02/19/2016] [Indexed: 11/22/2022] Open
Abstract
The dorsal column nuclei (DCN) of the brain stem contain secondary afferent neurons, which process ascending somatosensory information. Most of the known physiology of the DCN in primates has been acquired in acute experiments with anesthetized animals. Here, we developed a technique to implant a multielectrode array (MEA) chronically in the DCN of macaque monkeys to enable experiments with the animals awake. Two monkeys were implanted with brain-stem MEAs for 2-5 mo with no major adverse effects. Responses of the cuneate and gracile nuclei were quantified at the level of both field potentials and single units. Tactile receptive fields (RFs) were identified for 315 single units. A subset of these units had very regular spiking patterns with spike frequencies predominantly in the alpha band (8-14 Hz). The stability of the neuronal recordings was assessed with a novel analysis that identified units by their mean spike waveform and by the spike-triggered average of activity on all other electrodes in the array. Fifty-six identified neurons were observed over two or more sessions and in a few cases for as long as 1 mo. RFs of stable neurons were largely consistent across days. The results demonstrate that a chronic DCN implant in a macaque can be safe and effective, yielding high-quality unit recording for several months. The unprecedented access to these nuclei in awake primates should lead to a better understanding of their role in sensorimotor behavior.
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Affiliation(s)
- Andrew G Richardson
- Center for Neuroengineering and Therapeutics, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Pauline K Weigand
- Center for Neuroengineering and Therapeutics, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Srihari Y Sritharan
- Center for Neuroengineering and Therapeutics, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy H Lucas
- Center for Neuroengineering and Therapeutics, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Santello M, Bianchi M, Gabiccini M, Ricciardi E, Salvietti G, Prattichizzo D, Ernst M, Moscatelli A, Jörntell H, Kappers AML, Kyriakopoulos K, Albu-Schäffer A, Castellini C, Bicchi A. Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands. Phys Life Rev 2016; 17:1-23. [PMID: 26923030 DOI: 10.1016/j.plrev.2016.02.001] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 02/02/2016] [Indexed: 12/30/2022]
Abstract
The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.
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Affiliation(s)
- Marco Santello
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Matteo Bianchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Marco Gabiccini
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy; Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
| | - Emiliano Ricciardi
- Molecular Mind Laboratory, Dept. Surgical, Medical, Molecular Pathology and Critical Care, University of Pisa, Pisa, Italy; Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Gionata Salvietti
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Domenico Prattichizzo
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Marc Ernst
- Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany
| | - Alessandro Moscatelli
- Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany; Department of Systems Medicine and Centre of Space Bio-Medicine, Università di Roma "Tor Vergata", 00173, Rome, Italy
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | | | - Kostas Kyriakopoulos
- School of Mechanical Engineering, National Technical University of Athens, Greece
| | - Alin Albu-Schäffer
- DLR - German Aerospace Center, Institute of Robotics and Mechatronics, Oberpfaffenhofen, Germany
| | - Claudio Castellini
- DLR - German Aerospace Center, Institute of Robotics and Mechatronics, Oberpfaffenhofen, Germany
| | - Antonio Bicchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy.
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Abstract
Our tactile perception of external objects depends on skin-object interactions. The mechanics of contact dictates the existence of fundamental spatiotemporal input features—contact initiation and cessation, slip, and rolling contact—that originate from the fact that solid objects do not interpenetrate. However, it is unknown whether these features are represented within the brain. We used a novel haptic interface to deliver such inputs to the glabrous skin of finger/digit pads and recorded from neurons of the cuneate nucleus (the brain’s first level of tactile processing) in the cat. Surprisingly, despite having similar receptive fields and response properties, each cuneate neuron responded to a unique combination of these inputs. Hence, distinct haptic input features are encoded already at subcortical processing stages. This organization maps skin-object interactions into rich representations provided to higher cortical levels and may call for a re-evaluation of our current understanding of the brain’s somatosensory systems. Specific haptic input features were selectively delivered to glabrous skin The input features were segregated in the neurons of the cuneate nucleus These observations may call for a shift in current views of tactile processing
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Hayward V, Terekhov AV, Wong SC, Geborek P, Bengtsson F, Jörntell H. Spatio-temporal skin strain distributions evoke low variability spike responses in cuneate neurons. J R Soc Interface 2014; 11:20131015. [PMID: 24451390 PMCID: PMC3928934 DOI: 10.1098/rsif.2013.1015] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
A common method to explore the somatosensory function of the brain is to relate skin stimuli to neurophysiological recordings. However, interaction with the skin involves complex mechanical effects. Variability in mechanically induced spike responses is likely to be due in part to mechanical variability of the transformation of stimuli into spiking patterns in the primary sensors located in the skin. This source of variability greatly hampers detailed investigations of the response of the brain to different types of mechanical stimuli. A novel stimulation technique designed to minimize the uncertainty in the strain distributions induced in the skin was applied to evoke responses in single neurons in the cat. We show that exposure to specific spatio-temporal stimuli induced highly reproducible spike responses in the cells of the cuneate nucleus, which represents the first stage of integration of peripheral inputs to the brain. Using precisely controlled spatio-temporal stimuli, we also show that cuneate neurons, as a whole, were selectively sensitive to the spatial and to the temporal aspects of the stimuli. We conclude that the present skin stimulation technique based on localized differential tractions greatly reduces response variability that is exogenous to the information processing of the brain and hence paves the way for substantially more detailed investigations of the brain's somatosensory system.
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Affiliation(s)
- Vincent Hayward
- Sorbonne Universités, UPMC Univ Paris 06, , UMR 7222, ISIR, F-75005, Paris, France
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Bologna LL, Pinoteau J, Passot JB, Garrido JA, Vogel J, Vidal ER, Arleo A. A closed-loop neurobotic system for fine touch sensing. J Neural Eng 2013; 10:046019. [DOI: 10.1088/1741-2560/10/4/046019] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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