Deep Learning Approaches for Neural Decoding: from Cnns to Lstms and Spikes to Fmri
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Local Field Potential Decoding of the Onset and Intensity of Acute Pain In
www.nature.com/scientificreports OPEN Local feld potential decoding of the onset and intensity of acute pain in rats Received: 25 January 2018 Qiaosheng Zhang1, Zhengdong Xiao2,3, Conan Huang1, Sile Hu2,3, Prathamesh Kulkarni1,3, Accepted: 8 May 2018 Erik Martinez1, Ai Phuong Tong1, Arpan Garg1, Haocheng Zhou1, Zhe Chen 3,4 & Jing Wang1,4 Published: xx xx xxxx Pain is a complex sensory and afective experience. The current defnition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These defnitions can be subjective and do not take into consideration signals in the neural system. Local feld potentials (LFPs) represent summed electrical currents from multiple neurons in a defned brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the afective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifer with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. -
Improving Whole-Brain Neural Decoding of Fmri with Domain Adaptation
This is a repository copy of Improving whole-brain neural decoding of fMRI with domain adaptation. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/136411/ Version: Submitted Version Article: Zhou, S., Cox, C. and Lu, H. orcid.org/0000-0002-0349-2181 (Submitted: 2018) Improving whole-brain neural decoding of fMRI with domain adaptation. bioRxiv. (Submitted) https://doi.org/10.1101/375030 © 2018 The Author(s). For reuse permissions, please contact the Author(s). Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation Shuo Zhoua, Christopher R. Coxb,c, Haiping Lua,d,∗ aDepartment of Computer Science, the University of Sheffield, Sheffield, UK bSchool of Biological Sciences, the University of Manchester, Manchester, UK cDepartment of Psychology, Louisiana State University, Baton Rouge, Louisiana, USA dSheffield Institute for Translational Neuroscience, Sheffield, UK Abstract In neural decoding, there has been a growing interest in machine learning on whole-brain functional magnetic resonance imaging (fMRI). -
Topic 1 September 2015 – Volume 08, Issue 02
Topic 1 September 2015 – Volume 08, Issue 02 www.medical-neurosciences.de 22 Editorial CNSNewsletter September 2015 3 FOCUS Eating Right, Staying Bright 3 From Molecules to Mouthwatering Feast your eyes on the latest issue of the CNS Newsletter! If you’re reading this fi rst thing in the morning, have you 4 Boost Your Baby’s Brain! had breakfast yet? We can’t believe we’re saying this but 5 Breakfast and Brain Function if you haven’t, put the newsletter down and grab the most important meal of the day before doing anything else 6 The ABCs of Brain Foods (you’ll thank us when you get to page 5). 8 Our Moods, Our Foods What’s the hype about those shiny green “Bio” signs scattered across Berlin you pass by everyday on your 9 The Social Microbiome: A Gutsy Hypothesis morning commute? Read the low-down about organic 10 Aphrodisiacs and the Doctrine of Signatures food and the big debate about genetically modifi ed crops on pages 16 and 17. Are they any good for us? If you’re not 10 Are Oreos the New Cocaine? convinced, maybe you’re a natural skeptic. In that case: 11 Can You Be Addicted to Food? what do you think about the state of science? Is it in big trouble? Is it in need of profound change? Our brand new 12 Can Surgery Treat a Mental Illness? “Critique Corner” features an anonymous contributor dis- cussing the gloomier parts of contemporary science. 13 Ceasing Seizures with Fat? We have an inspiring interview with the man who 14 Dieting and the Brain brought us even more excitement than is usually expected from a FIFA World Cup opening match. -
Neural Decoding of Collective Wisdom with Multi-Brain Computing
NeuroImage 59 (2012) 94–108 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Review Neural decoding of collective wisdom with multi-brain computing Miguel P. Eckstein a,b,⁎, Koel Das a,b, Binh T. Pham a, Matthew F. Peterson a, Craig K. Abbey a, Jocelyn L. Sy a, Barry Giesbrecht a,b a Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, 93101, USA b Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, 93101, USA article info abstract Article history: Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon Received 7 April 2011 known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its Revised 27 June 2011 benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and Accepted 4 July 2011 multi-brain computing with twenty humans making perceptual decisions to show that combining neural Available online 14 July 2011 activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision Keywords: Multi-brain computing variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in Collective wisdom substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme Neural decoding opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and Perceptual decisions time-points while increasing number of brains demonstrate unique benefits arising from integrating neural Group decisions activity across different brains. -
Download a Copy of the 264-Page Publication
2020 Department of Neurological Surgery Annual Report Reporting period July 1, 2019 through June 30, 2020 Table of Contents: Introduction .................................................................3 Faculty and Residents ...................................................5 Faculty ...................................................................6 Residents ...............................................................8 Stuart Rowe Lecturers .........................................10 Peter J. Jannetta Lecturers ................................... 11 Department Overview ............................................... 13 History ............................................................... 14 Goals/Mission .................................................... 16 Organization ...................................................... 16 Accomplishments of Note ................................ 29 Education Programs .................................................. 35 Faculty Biographies ................................................... 47 Resident Biographies ................................................171 Research ....................................................................213 Overview ...........................................................214 Investigator Research Summaries ................... 228 Research Grant Summary ................................ 242 Alumni: Past Residents ........................................... 249 Donations ................................................................ 259 Statistics -
Implantable Microelectrodes on Soft Substrate with Nanostructured Active Surface for Stimulation and Recording of Brain Activities Valentina Castagnola
Implantable microelectrodes on soft substrate with nanostructured active surface for stimulation and recording of brain activities Valentina Castagnola To cite this version: Valentina Castagnola. Implantable microelectrodes on soft substrate with nanostructured active sur- face for stimulation and recording of brain activities. Micro and nanotechnologies/Microelectronics. Universite Toulouse III Paul Sabatier, 2014. English. tel-01137352 HAL Id: tel-01137352 https://hal.archives-ouvertes.fr/tel-01137352 Submitted on 31 Mar 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSETHÈSE En vue de l’obtention du DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE Délivré par : l’Université Toulouse 3 Paul Sabatier (UT3 Paul Sabatier) Présentée et soutenue le 18/12/2014 par : Valentina CASTAGNOLA Implantable Microelectrodes on Soft Substrate with Nanostructured Active Surface for Stimulation and Recording of Brain Activities JURY M. Frédéric MORANCHO Professeur d’Université Président de jury M. Blaise YVERT Directeur de recherche Rapporteur Mme Yael HANEIN Professeur d’Université Rapporteur M. Pascal MAILLEY Directeur de recherche Examinateur M. Christian BERGAUD Directeur de recherche Directeur de thèse Mme Emeline DESCAMPS Chargée de Recherche Directeur de thèse École doctorale et spécialité : GEET : Micro et Nanosystèmes Unité de Recherche : Laboratoire d’Analyse et d’Architecture des Systèmes (UPR 8001) Directeur(s) de Thèse : M. -
Human Enhancement Technologies and Our Merger with Machines
Human Enhancement and Technologies Our Merger with Machines Human • Woodrow Barfield and Blodgett-Ford Sayoko Enhancement Technologies and Our Merger with Machines Edited by Woodrow Barfield and Sayoko Blodgett-Ford Printed Edition of the Special Issue Published in Philosophies www.mdpi.com/journal/philosophies Human Enhancement Technologies and Our Merger with Machines Human Enhancement Technologies and Our Merger with Machines Editors Woodrow Barfield Sayoko Blodgett-Ford MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Editors Woodrow Barfield Sayoko Blodgett-Ford Visiting Professor, University of Turin Boston College Law School Affiliate, Whitaker Institute, NUI, Galway USA Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Philosophies (ISSN 2409-9287) (available at: https://www.mdpi.com/journal/philosophies/special issues/human enhancement technologies). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Volume Number, Page Range. ISBN 978-3-0365-0904-4 (Hbk) ISBN 978-3-0365-0905-1 (PDF) Cover image courtesy of N. M. Ford. © 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. -
Provably Optimal Design of a Brain-Computer Interface Yin Zhang
Provably Optimal Design of a Brain-Computer Interface Yin Zhang CMU-RI-TR-18-63 August 2018 The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Steven M. Chase, Carnegie Mellon University, Chair Robert E. Kass, Carnegie Mellon University J. Andrew Bagnell, Carnegie Mellon University Patrick J. Loughlin, University of Pittsburgh Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics. Copyright c 2018 Yin Zhang Keywords: Brain-Computer Interface, Closed-Loop System, Optimal Control Abstract Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into movement of the device in order to achieve the most pro- ficient control? This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device, to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to per- formance that might be achieved with a non-biomimetic device, once the subject has engaged in extended practice and learned how to use it. In this thesis, I first recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. -
Neural Networks for Efficient Bayesian Decoding of Natural Images From
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons Nikhil Parthasarathy∗ Eleanor Batty∗ William Falcon Stanford University Columbia University Columbia University [email protected] [email protected] [email protected] Thomas Rutten Mohit Rajpal E.J. Chichilniskyy Columbia University Columbia University Stanford University [email protected] [email protected] [email protected] Liam Paninskiy Columbia University [email protected] Abstract Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or ex- ploit the fidelity of the neural signal. Here we develop a new approximate Bayesian method for decoding natural images from the spiking activity of populations of retinal ganglion cells (RGCs). We sidestep known computational challenges with Bayesian inference by exploiting artificial neural networks developed for computer vision, enabling fast nonlinear decoding that incorporates natural scene statistics implicitly. We use a decoder architecture that first linearly reconstructs an image from RGC spikes, then applies a convolutional autoencoder to enhance the image. The resulting decoder, trained on natural images and simulated neural responses, significantly outperforms linear decoding, as well as simple point-wise nonlinear decoding. These results provide a tool for the assessment and optimization of reti- nal prosthesis technologies, and reveal that the retina may provide a more accurate representation of the visual scene than previously appreciated. 1 Introduction Neural coding in sensory systems is often studied by developing and testing encoding models that capture how sensory inputs are represented in neural signals. -
Attempts to Achieve Immortality
Attempts to achieve immortality Attempts to achieve immortality Author: Stepanka Boudova E-mail: [email protected] Student ID: 419572 Masaryk University Brno Course: Future of Informatics 1 Attempts to achieve immortality Table of Contents Attempts to achieve immortality......................................................................................................1 Introduction......................................................................................................................................3 Historical context.............................................................................................................................3 History of Alchemy.....................................................................................................................3 Religion and Immortality............................................................................................................ 5 Symbols of Immortality.............................................................................................................. 6 The future.........................................................................................................................................6 Mind Uploading.......................................................................................................................... 6 Cryonics.................................................................................................................................... 10 2 Attempts to achieve immortality Introduction -
Enhancing Nervous System Recovery Through Neurobiologics, Neural Interface Training, and Neurorehabilitation
UCLA UCLA Previously Published Works Title Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation. Permalink https://escholarship.org/uc/item/0rb4m424 Authors Krucoff, Max O Rahimpour, Shervin Slutzky, Marc W et al. Publication Date 2016 DOI 10.3389/fnins.2016.00584 Peer reviewed eScholarship.org Powered by the California Digital Library University of California REVIEW published: 27 December 2016 doi: 10.3389/fnins.2016.00584 Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation Max O. Krucoff 1*, Shervin Rahimpour 1, Marc W. Slutzky 2, 3, V. Reggie Edgerton 4 and Dennis A. Turner 1, 5, 6 1 Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA, 2 Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA, 3 Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA, 4 Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA, 5 Department of Neurobiology, Duke University Medical Center, Durham, NC, USA, 6 Research and Surgery Services, Durham Veterans Affairs Medical Center, Durham, NC, USA After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, Edited by: neurobiologists have been identifying and manipulating components of the intra- and Ioan Opris, University of Miami, USA extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have Reviewed by: been producing brain-machine and neural interfaces that circumvent lesions to restore Yoshio Sakurai, functionality, and neurorehabilitation experts have been developing new ways to revitalize Doshisha University, Japan Brian R. -
KAFUI DZIRASA, MD Phd
KAFUI DZIRASA, MD PhD 421 Bryan Research, Box 3209 E-mail: [email protected] 311 Research Drive Durham, North Carolina 27710 Duke University Medical Center Office: (919) 681-7371 EDUCATION: University of Maryland Baltimore County, Baltimore, MD June 1996-May 2001 Bachelor of Science in Chemical Engineering, Magna cum laude Meyerhoff Scholar Duke University Graduate School, Durham NC August 2003-March 2007 Doctor of Philosophy in Neurobiology Thesis: Electrophysiological Correlates of Neurological and Psychiatric Disease Advisor: Miguel Nicolelis Postdoctoral Research August 2007-March 2009 Duke University, Durham, NC Advisor: Miguel Nicolelis Duke University School of Medicine, Durham, NC August 2001-May 2009 Doctor of Medicine Medical Scientist Training Program Duke University Hospital, Durham, NC July 2010-June 2016 Residency, General Psychiatry North Carolina Medical License License #: 2016-02599 License Status: Active EMPLOYMENT HISTORY: K. Ranga Rama Krishnan Endowed Associate Professor with Tenure, Department of Psychiatry and Behavioral Sciences. Duke University Medical Center. Aug 2019-Current K. Ranga Rama Krishnan Endowed Associate Professor, Tenure Track, Department of Psychiatry and Behavioral Sciences. Duke University Medical Center. May 2018-July 2019 Associate Professor, Tenure Track, Department of Psychiatry and Behavioral Sciences. Duke University Medical Center. September 2017-May 2018 Assistant Professor, Tenure Track, Department of Psychiatry and Behavioral Sciences. Duke University Medical Center. December 2013-August 2017 Assistant Research Professor, Department of Neurobiology. Duke University. October 2012-Current. Assistant Professor, Department of Biomedical Engineering. Duke University. March 2012-Current. Visiting Professor of Neuroscience, Edmond and Lily Safra International Institute of Neurosciences of Natal (ELS-IINN). August 2011-2019. General Psychiatry Resident, Department of Psychiatry and Behavioral Sciences.