What Is a Representative Brain? Neuroscience Meets Population Science
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Neuro Informatics 2020
Neuro Informatics 2019 September 1-2 Warsaw, Poland PROGRAM BOOK What is INCF? About INCF INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology in Stockholm, Sweden. INCF currently has Governing and Associate Nodes spanning 4 continents, with an extended network comprising organizations, individual researchers, industry, and publishers. INCF promotes the implementation of neuroinformatics and aims to advance data reuse and reproducibility in global brain research by: • developing and endorsing community standards and best practices • leading the development and provision of training and educational resources in neuroinformatics • promoting open science and the sharing of data and other resources • partnering with international stakeholders to promote neuroinformatics at global, national and local levels • engaging scientific, clinical, technical, industry, and funding partners in colla- borative, community-driven projects INCF supports the FAIR (Findable Accessible Interoperable Reusable) principles, and strives to implement them across all deliverables and activities. Learn more: incf.org neuroinformatics2019.org 2 Welcome Welcome to the 12th INCF Congress in Warsaw! It makes me very happy that a decade after the 2nd INCF Congress in Plzen, Czech Republic took place, for the second time in Central Europe, the meeting comes to Warsaw. The global neuroinformatics scenery has changed dramatically over these years. With the European Human Brain Project, the US BRAIN Initiative, Japanese Brain/ MINDS initiative, and many others world-wide, neuroinformatics is alive more than ever, responding to the demands set forth by the modern brain studies. -
Artificial Brain Project of Visual Motion
In this issue: • Editorial: Feeding the senses • Supervised learning in spiking neural networks V o l u m e 3 N u m b e r 2 M a r c h 2 0 0 7 • Dealing with unexpected words • Embedded vision system for real-time applications • Can spike-based speech Brain-inspired auditory recognition systems outperform conventional approaches? processor and the • Book review: Analog VLSI circuits for the perception Artificial Brain project of visual motion The Korean Brain Neuroinformatics Re- search Program has two goals: to under- stand information processing mechanisms in biological brains and to develop intel- ligent machines with human-like functions based on these mechanisms. We are now developing an integrated hardware and software platform for brain-like intelligent systems called the Artificial Brain. It has two microphones, two cameras, and one speaker, looks like a human head, and has the functions of vision, audition, inference, and behavior (see Figure 1). The sensory modules receive audio and video signals from the environment, and perform source localization, signal enhancement, feature extraction, and user recognition in the forward ‘path’. In the backward path, top-down attention is per- formed, greatly improving the recognition performance of real-world noisy speech and occluded patterns. The fusion of audio and visual signals for lip-reading is also influenced by this path. The inference module has a recurrent architecture with internal states to imple- ment human-like emotion and self-esteem. Also, we would like the Artificial Brain to eventually have the abilities to perform user modeling and active learning, as well as to be able to ask the right questions both to the right people and to other Artificial Brains. -
Identifiable Neuro Ethics Challenges to the Banking of Neuro Data
ILLES J, LOMBERA S. IDENTIFIABLE NEURO ETHICS CHALLENGES TO THE BANKING OF NEURO DATA. MINN. J.L. SCI. & TECH. 2009;10(1):71-94. Identifiable Neuro Ethics Challenges to the Banking of Neuro Data Judy Illes ∗ & Sofia Lombera** Laboratory and clinical investigations about the brain and behavioral sciences, broadly defined as “neuroscience,” have advanced the understanding of how people think, move, feel, plan and more, both in good health and when suffering from a neurologic or psychiatric disease. Shared databases built on information obtained from neuroscience discoveries hold true promise for advancing the knowledge of brain function by leveraging new possibilities for combining complex and diverse data.1 Accompanying these opportunities are ethics challenges that, in other domains like the sharing of genetic information, have an impact on all parties involved in the research enterprise. The ethics and policy challenges include regulating the content of, access to, and use of databases; ensuring that data remains confidential and that informed consent © 2009 Judy Illes and Sofia Lombera. ∗ Judy Illes, Ph.D., Corresponding author, National Core for Neuroethics, The University of British Columbia. The authors are grateful to Dr. Jack Van Horn, Dr. Peter Reiner, Patricia Lau, and Daniel Buchman for valuable discussions on the future of neuro data banks. Parts of this paper were presented at the conference “Emerging Problems in Neurogenomics: Ethical, Legal & Policy Issues at the Intersection of Genomics & Neuroscience,” February 29, 2008, University of Minnesota, Minneapolis, Minnesota by Judy Illes. Full video of that conference is available at http://www.lifesci.consortium.umn.edu/conference/neuro.php. Supported by CIHR/INMHA CNE-85117, CFI, BCKDF, and NIH/NIMH # 9R01MH84282- 04A1. -
New Technologies for Human Robot Interaction and Neuroprosthetics
University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Engineering, Computing and Mathematics 2017-07-01 Human-Robot Interaction and Neuroprosthetics: A review of new technologies Cangelosi, A http://hdl.handle.net/10026.1/9872 10.1109/MCE.2016.2614423 IEEE Consumer Electronics Magazine All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. CEMAG-OA-0004-Mar-2016.R3 1 New Technologies for Human Robot Interaction and Neuroprosthetics Angelo Cangelosi, Sara Invitto Abstract—New technologies in the field of neuroprosthetics and These developments in neuroprosthetics are closely linked to robotics are leading to the development of innovative commercial the recent significant investment and progress in research on products based on user-centered, functional processes of cognitive neural networks and deep learning approaches to robotics and neuroscience and perceptron studies. The aim of this review is to autonomous systems [2][3]. Specifically, one key area of analyze this innovative path through the description of some of the development has been that of cognitive robots for human-robot latest neuroprosthetics and human-robot interaction applications, in particular the Brain Computer Interface linked to haptic interaction and assistive robotics. This concerns the design of systems, interactive robotics and autonomous systems. These robot companions for the elderly, social robots for children with issues will be addressed by analyzing developmental robotics and disabilities such as Autism Spectrum Disorders, and robot examples of neurorobotics research. -
On the Technology Prospects and Investment Opportunities for Scalable Neuroscience
On the Technology Prospects and Investment Opportunities for Scalable Neuroscience Thomas Dean1,2,3 Biafra Ahanonu3 Mainak Chowdhury3 Anjali Datta3 Andre Esteva3 Daniel Eth3 Nobie Redmon3 Oleg Rumyantsev3 Ysis Tarter3 1 Google Research, 2 Brown University, 3 Stanford University Contents 1 Executive Summary 1 2 Introduction 4 3 Evolving Imaging Technologies 6 4 Macroscale Reporting Devices 10 5 Automating Systems Neuroscience 14 6 Synthetic Neurobiology 16 7 Nanotechnology 20 8 Acknowledgements 28 A Leveraging Sequencing for Recording — Biafra Ahanonu 28 B Scalable Analytics and Data Mining — Mainak Chowdhury 32 C Macroscale Imaging Technologies — Anjali Datta 35 D Nanoscale Recording and Wireless Readout — Andre Esteva 38 E Hybrid Biological and Nanotechnology Solutions — Daniel Eth 41 F Advances in Contrast Agents and Tissue Preparation — Nobie Redmon 44 G Microendoscopy and Optically Coupled Implants — Oleg Rumyantsev 46 H Opportunities for Automating Laboratory Procedures — Ysis Tarter 49 i 1 Executive Summary Two major initiatives to accelerate research in the brain sciences have focused attention on devel- oping a new generation of scientific instruments for neuroscience. These instruments will be used to record static (structural) and dynamic (behavioral) information at unprecedented spatial and temporal resolution and report out that information in a form suitable for computational analysis. We distinguish between recording — taking measurements of individual cells and the extracellu- lar matrix — and reporting — transcoding, packaging and transmitting the resulting information for subsequent analysis — as these represent very different challenges as we scale the relevant technologies to support simultaneously tracking the many neurons that comprise neural circuits of interest. We investigate a diverse set of technologies with the purpose of anticipating their devel- opment over the span of the next 10 years and categorizing their impact in terms of short-term [1-2 years], medium-term [2-5 years] and longer-term [5-10 years] deliverables. -
Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: a Systematic Literature Review
sensors Systematic Review Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review Nuraini Jamil 1 , Abdelkader Nasreddine Belkacem 2,* , Sofia Ouhbi 1 and Abderrahmane Lakas 2 1 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] (N.J.); sofi[email protected] (S.O.) 2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] * Correspondence: [email protected] Abstract: Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive Citation: Jamil, N.; Belkacem, A.N.; BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used Ouhbi, S.; Lakas, A. Noninvasive for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from Electroencephalography Equipment the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited for Assistive, Adaptive, and to studies focusing on applications and devices of the BCI technology. -
What Are Computational Neuroscience and Neuroinformatics? Computational Neuroscience
Department of Mathematical Sciences B12412: Computational Neuroscience and Neuroinformatics What are Computational Neuroscience and Neuroinformatics? Computational Neuroscience Computational Neuroscience1 is an interdisciplinary science that links the diverse fields of neu- roscience, computer science, physics and applied mathematics together. It serves as the primary theoretical method for investigating the function and mechanism of the nervous system. Com- putational neuroscience traces its historical roots to the the work of people such as Andrew Huxley, Alan Hodgkin, and David Marr. Hodgkin and Huxley's developed the voltage clamp and created the first mathematical model of the action potential. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory. Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning,neural networks and statistical learning theory in that it emphasizes descriptions of functional and biologically realistic neurons and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network os- cillations and learning and memory. These computational models are used to test hypotheses that can be directly verified by current or future biological experiments. Currently, the field is undergoing a rapid expansion. There are many software packages, such as NEURON, that allow rapid and systematic in silico modeling of realistic neurons. -
Introduction of Optical Coherence Tomography
UC Riverside UC Riverside Electronic Theses and Dissertations Title Phase Resolved Optical Coherence Tomography for Label-Free Detection of Neural Activity Permalink https://escholarship.org/uc/item/43g020nw Author Tong, Minh Publication Date 2018 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA RIVERSIDE Phase Resolved Optical Coherence Tomography for Label-Free Detection of Neural Activity A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Neuroscience by Minh Quang Tong December 2018 Dissertation Committee: Dr. B. Hyle Park, Co-Chairperson Dr. Michael E Adams, Co-Chairperson Dr. Hongdian Yang Copyright by Minh Quang Tong 2018 The Dissertation of Minh Quang Tong is approved: Committee Co-Chairperson Committee Co-Chairperson University of California, Riverside ACKNOWLEDGEMENTS Thank you, Dr. B. Hyle Park, for your patience and guidance during that last four years. Your patience is of a saint. You were able to handle all my mistakes, even those that resulted in the failure of an entire optical system. Your guidance is of a light house. You were there to provided valuable suggestions but also allowed me to explore new ideas. All this work would not be possible without your support. I wish your family the best! I would also like to thank my dissertation committee members, Dr. Michael Adams and Dr. Hongdian Yang, for their time to review my project through the years and providing valuable feedback. Dr. Michael Adams has been knowledgeable and supportive through my entire graduate school life, without him I would be lost, thank you so much. -
Bayesian Inference for Biophysical Neuron Models Enables Stimulus
RESEARCH ARTICLE Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics Jonathan Oesterle1, Christian Behrens1, Cornelius Schro¨ der1, Thoralf Hermann2, Thomas Euler1,3,4, Katrin Franke1,4, Robert G Smith5, Gu¨ nther Zeck2, Philipp Berens1,3,4,6* 1Institute for Ophthalmic Research, University of Tu¨ bingen, Tu¨ bingen, Germany; 2Naturwissenschaftliches und Medizinisches Institut an der Universita¨ t Tu¨ bingen, Reutlingen, Germany; 3Center for Integrative Neuroscience, University of Tu¨ bingen, Tu¨ bingen, Germany; 4Bernstein Center for Computational Neuroscience, University of Tu¨ bingen, Tu¨ bingen, Germany; 5Department of Neuroscience, University of Pennsylvania, Philadelphia, United States; 6Institute for Bioinformatics and Medical Informatics, University of Tu¨ bingen, Tu¨ bingen, Germany Abstract While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics. *For correspondence: [email protected] Competing interests: The Introduction authors declare that no Mechanistic models have been extensively used to study the biophysics underlying information proc- competing interests exist. -
The Role of Conceptual Knowledge and Cognitive Control in A
Running Head: SOCIAL SEMANTICS Social Semantics: The role of conceptual knowledge and cognitive control in a neurobiological model of the social brain Richard J. Binney1* and Richard Ramsey1,2*† 1Wales Institute for Cognitive Neuroscience, School of Psychology, Bangor University 2 Department of Psychology, Macquarie University † The two authors made an equal contribution to this manuscript. *Address Correspondence to: Richard J. Binney, PhD or Richard Ramsey, PhD Wales Institute for Cognitive Neuroscience School of Psychology Bangor University Gwynedd, LL57 2AS Wales, UK Email: [email protected]/[email protected] Tel: +44 (0) 1248 383 478 Acknowledgements: The authors would like to thank Robert Ward and Emily S. Cross for their comments on a previous version of this manuscript. Disclosure: The authors declare no potential conflicts of interest. Word Count (not including abstract): 7569 Abstract Word Count: 169 Number of Figures: 2 Number of Tables: 0 SOCIAL SEMANTICS 2 Abstract Research in social neuroscience has primarily focused on carving up cognition into distinct pieces, as a function of mental process, neural network or social behaviour, while the need for unifying models that span multiple social phenomena has been relatively neglected. Here we present a novel framework that treats social cognition as a case of semantic cognition, which provides a neurobiologically constrained and generalizable framework, with clear, testable predictions regarding sociocognitive processing in the context of both health and disease. According to this framework, social cognition relies on two principal systems of representation and control. These systems are neuroanatomically and functionally distinct, but interact to (1) enable development of foundational, conceptual-level knowledge and (2) regulate access to this information in order to generate flexible and context-appropriate social behaviour. -
Best Practices in Data Analysis and Sharing in Neuroimaging Using MRI
Best Practices in Data Analysis and Sharing in Neuroimaging using MRI as E. Nichols1,*, Samir Das 2, Simon B. Eickhoff 3, Alan C. Evans 2, Tristan G 2 Thom latard , ichael Hanke4, Nikolaus Kriegeskorte 5, Michael P. M 6, Russell A. Poldrack 7 M ilham , JeanBaptiste Poline8, Erika Proal 9, Bertrand T 10, David C. Van Essen 11, Tonya W 12 hirion hite , . T as Yeo13 B.T hom 1 arwick; 2 University; 3 Düsseldorf; University of W McGill Heinrich-Heine University 4 agdeburg; 5 Unit; 6 Otto-von-Guericke-University M MRC Cognition and Brain Sciences Child ind Institute; 7Stanford University; 8University of California, Berkeley; M 9 Neuroingenia; 10 Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz & Inria, University; 11 Louis; 12 Paris-Saclay Washington University in St. Erasmus University Medical Center; 13National University of Singapore. *Correspondence: [email protected]. Abstract Neuroimaging enables rich noninvasive measurements of human brain activity, but translating such data into neuroscientific insights and clinical applications requires complex analyses and collaboration among a diverse array of researchers. The open science movement is reshaping scientific culture and addressing the challenges of transparency and reproducibility of research. To advance open science in neuroimaging the Organization for Human Brain Mapping created the Committee on Best Practice in Data Analysis and Sharing (COBIDAS), charged with creating a report that collects best practice recommendations from experts and the entire brain imaging community. The purpose of this work is to elaborate the principles of open and reproducible research for neuroimaging using Magnetic Resonance Imaging (MRI), and then distill these principles to specific research practices. -
Data Sharing in Neuroimaging Research
Data Sharing In Neuroimaging Research The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Poline, Jean-Baptiste, Janis L. Breeze, Satrajit Ghosh, Krzysztof Gorgolewski, Yaroslav O. Halchenko, Michael Hanke, Christian Haselgrove, et al. 2012. Data sharing in neuroimaging research. Frontiers in Neuroinformatics 6(9). Published Version doi:10.3389/fninf.2012.00009 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:10345106 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA REVIEW ARTICLE published: 05 April 2012 NEUROINFORMATICS doi: 10.3389/fninf.2012.00009 Data sharing in neuroimaging research Jean-Baptiste Poline 1,2*, Janis L. Breeze 3, Satrajit Ghosh 4, Krzysztof Gorgolewski 5, Yaroslav O. Halchenko 6, Michael Hanke 7, Christian Haselgrove 8 , Karl G. Helmer 9, David B. Keator 10 , Daniel S. Marcus 11 , Russell A. Poldrack 12, Yannick Schwartz 1, John Ashburner 13 and David N. Kennedy 8 1 Neurospin, Commissariat à l’Energie Atomique et aux Energies Alternatives, Gif-sur-Yvette, France 2 Brain Imaging Centre, University of California at Berkeley, Berkeley, CA, USA 3 International Neuroinformatics Coordinating Facility, Karolinska Institute, Stockholm, Sweden 4 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge,