Neurons, Action Potentials, and Synapses - Simo S
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Creation of Neuroscience
The Creation of Neuroscience The Society for Neuroscience and the Quest for Disciplinary Unity 1969-1995 Introduction rom the molecular biology of a single neuron to the breathtakingly complex circuitry of the entire human nervous system, our understanding of the brain and how it works has undergone radical F changes over the past century. These advances have brought us tantalizingly closer to genu- inely mechanistic and scientifically rigorous explanations of how the brain’s roughly 100 billion neurons, interacting through trillions of synaptic connections, function both as single units and as larger ensem- bles. The professional field of neuroscience, in keeping pace with these important scientific develop- ments, has dramatically reshaped the organization of biological sciences across the globe over the last 50 years. Much like physics during its dominant era in the 1950s and 1960s, neuroscience has become the leading scientific discipline with regard to funding, numbers of scientists, and numbers of trainees. Furthermore, neuroscience as fact, explanation, and myth has just as dramatically redrawn our cultural landscape and redefined how Western popular culture understands who we are as individuals. In the 1950s, especially in the United States, Freud and his successors stood at the center of all cultural expla- nations for psychological suffering. In the new millennium, we perceive such suffering as erupting no longer from a repressed unconscious but, instead, from a pathophysiology rooted in and caused by brain abnormalities and dysfunctions. Indeed, the normal as well as the pathological have become thoroughly neurobiological in the last several decades. In the process, entirely new vistas have opened up in fields ranging from neuroeconomics and neurophilosophy to consumer products, as exemplified by an entire line of soft drinks advertised as offering “neuro” benefits. -
Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2015 Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications Shaun Donachy Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Theory and Algorithms Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/3984 This Thesis is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. c Shaun Donachy, July 2015 All Rights Reserved. SPIKING NEURAL NETWORKS: NEURON MODELS, PLASTICITY, AND GRAPH APPLICATIONS A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. by SHAUN DONACHY B.S. Computer Science, Virginia Commonwealth University, May 2013 Director: Krzysztof J. Cios, Professor and Chair, Department of Computer Science Virginia Commonwealth University Richmond, Virginia July, 2015 TABLE OF CONTENTS Chapter Page Table of Contents :::::::::::::::::::::::::::::::: i List of Figures :::::::::::::::::::::::::::::::::: ii Abstract ::::::::::::::::::::::::::::::::::::: v 1 Introduction ::::::::::::::::::::::::::::::::: 1 2 Models of a Single Neuron ::::::::::::::::::::::::: 3 2.1 McCulloch-Pitts Model . 3 2.2 Hodgkin-Huxley Model . 4 2.3 Integrate and Fire Model . 6 2.4 Izhikevich Model . 8 3 Neural Coding Techniques :::::::::::::::::::::::::: 14 3.1 Input Encoding . 14 3.1.1 Grandmother Cell and Distributed Representations . 14 3.1.2 Rate Coding . 15 3.1.3 Sine Wave Encoding . 15 3.1.4 Spike Density Encoding . 15 3.1.5 Temporal Encoding . 16 3.1.6 Synaptic Propagation Delay Encoding . -
Neuron C Reference Guide Iii • Introduction to the LONWORKS Platform (078-0391-01A)
Neuron C Provides reference info for writing programs using the Reference Guide Neuron C programming language. 078-0140-01G Echelon, LONWORKS, LONMARK, NodeBuilder, LonTalk, Neuron, 3120, 3150, ShortStack, LonMaker, and the Echelon logo are trademarks of Echelon Corporation that may be registered in the United States and other countries. Other brand and product names are trademarks or registered trademarks of their respective holders. Neuron Chips and other OEM Products were not designed for use in equipment or systems, which involve danger to human health or safety, or a risk of property damage and Echelon assumes no responsibility or liability for use of the Neuron Chips in such applications. Parts manufactured by vendors other than Echelon and referenced in this document have been described for illustrative purposes only, and may not have been tested by Echelon. It is the responsibility of the customer to determine the suitability of these parts for each application. ECHELON MAKES AND YOU RECEIVE NO WARRANTIES OR CONDITIONS, EXPRESS, IMPLIED, STATUTORY OR IN ANY COMMUNICATION WITH YOU, AND ECHELON SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of Echelon Corporation. Printed in the United States of America. Copyright © 2006, 2014 Echelon Corporation. Echelon Corporation www.echelon.com Welcome This manual describes the Neuron® C Version 2.3 programming language. It is a companion piece to the Neuron C Programmer's Guide. -
Oligodendrocytes in Development, Myelin Generation and Beyond
cells Review Oligodendrocytes in Development, Myelin Generation and Beyond Sarah Kuhn y, Laura Gritti y, Daniel Crooks and Yvonne Dombrowski * Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast BT9 7BL, UK; [email protected] (S.K.); [email protected] (L.G.); [email protected] (D.C.) * Correspondence: [email protected]; Tel.: +0044-28-9097-6127 These authors contributed equally. y Received: 15 October 2019; Accepted: 7 November 2019; Published: 12 November 2019 Abstract: Oligodendrocytes are the myelinating cells of the central nervous system (CNS) that are generated from oligodendrocyte progenitor cells (OPC). OPC are distributed throughout the CNS and represent a pool of migratory and proliferative adult progenitor cells that can differentiate into oligodendrocytes. The central function of oligodendrocytes is to generate myelin, which is an extended membrane from the cell that wraps tightly around axons. Due to this energy consuming process and the associated high metabolic turnover oligodendrocytes are vulnerable to cytotoxic and excitotoxic factors. Oligodendrocyte pathology is therefore evident in a range of disorders including multiple sclerosis, schizophrenia and Alzheimer’s disease. Deceased oligodendrocytes can be replenished from the adult OPC pool and lost myelin can be regenerated during remyelination, which can prevent axonal degeneration and can restore function. Cell population studies have recently identified novel immunomodulatory functions of oligodendrocytes, the implications of which, e.g., for diseases with primary oligodendrocyte pathology, are not yet clear. Here, we review the journey of oligodendrocytes from the embryonic stage to their role in homeostasis and their fate in disease. We will also discuss the most common models used to study oligodendrocytes and describe newly discovered functions of oligodendrocytes. -
Polyribosomes Associated with Synaptic Specializations on Axon Initial Segments: Localization of Protein-Synthetic Machinery at Inhibitory Synapses
The Journal of Neuroscience October 1986, 6(10): 3079-3085 Polyribosomes Associated with Synaptic Specializations on Axon Initial Segments: Localization of Protein-Synthetic Machinery at Inhibitory Synapses Oswald Steward* and Charles E. Ribak-f *Departments of Neurosurgery and Physiology, University of Virginia School of Medicine, Charlottesville, Virginia 22908, and TDepartment of Anatomy and Neurobiology, University of California at Irvine, Irvine, California 92717 Previous studies have revealed a selective association between report that showed some axonal ribosomes grouped at points polyribosomes and axospinous synapses in a variety of brain of branching and beneath synaptic contacts (Jones and Powell, regions. The present study evaluates whether polyribosomes are 1969). Therefore, they were usually considered, only in passing, also associated with the symmetrical and presumably inhibitory as exceptions to the rule. Thus, it has come to be accepted as a synaptic connections found on the initial segment of axons of central tenet that essentially all of the protein constituents of some neurons in the CNS. The initial segments of pyramidal the neuron, including those required for synaptic specializations, neurons in the sensorimotor cortex of the monkey and of granule were synthesized in the cell body, and somehow selectively cells in the hippocampus of the rat were examined. The initial transported to the sites at which they would be assembled (for segments of these cell types are contacted by GABAergic ter- a review, see Grafstein and Forman, 1980). minals that form symmetrical synaptic connections. In the pres- Over the past few years, a series of observations has given ent study, these initial segments were found to contain polyri- rise to an hypothesis that challenges the prevailing view that bosomes that tended to be selectively localized beneath the virtually all neuronal proteins are produced in the cell body and synaptic specializations. -
Nernst Potentials and Membrane Potential Changes
UNDERSTANDING MEMBRANE POTENTIAL CHANGES IN TERMS OF NERNST POTENTIALS: For seeing how a change in conductance to ions affects the membrane potential, follow these steps: 1. Make a graph with membrane potential on the vertical axis (-100 to +55) and time on the horizontal axis. 2. Draw dashed lines indicating the standard Nernst potential (equilibrium potential) for each ion: Na+ = +55 mV, K+ = -90mV, Cl- = -65 mV. 3. Draw lines below the horizontal axis showing the increased conductance to individual ions. 4. Start plotting the membrane potential on the left. Most graphs will start at resting potential (-70 mV) 5. When current injection (Stim) is present, move the membrane potential upward to Firing threshold. 6. For the time during which membrane conductance to a particular ion increases, move the membrane potential toward the Nernst potential for that ion. 7. During the time when conductance to a particular ion decreases, move the membrane potential away from the Nernst potential of that ion, toward a position which averages the conductances of the other ions. 8. When conductances return to their original value, membrane potential will go to its starting value. +55mV ACTION POTENTIAL SYNAPTIC POTENTIALS 0mV Membrane potential Firing threshold Firing threshold -65mV -90mV Time Time Na+ K+ Conductances Stim CHANGES IN MEMBRANE POTENTIAL ALLOW NEURONS TO COMMUNICATE The membrane potential of a neuron can be measured with an intracellular electrode. This 1 provides a measurement of the voltage difference between the inside of the cell and the outside. When there is no external input, the membrane potential will usually remain at a value called the resting potential. -
Rapid and Efficient Generation of Oligodendrocytes from Human
Rapid and efficient generation of oligodendrocytes PNAS PLUS from human induced pluripotent stem cells using transcription factors Marc Ehrlicha,b, Sabah Mozafaric,d,e,f, Michael Glatzab, Laura Starosta,b, Sergiy Velychkob, Anna-Lena Hallmanna,b, Qiao-Ling Cuig, Axel Schambachh, Kee-Pyo Kimb, Corinne Bachelinc,d,e,f, Antoine Marteync,d,e,f, Gunnar Hargusa,b, Radia Marie Johnsoni, Jack Antelg, Jared Sterneckertj, Holm Zaehresb,k, Hans R. Schölerb,l, Anne Baron-Van Evercoorenc,d,e,f, and Tanja Kuhlmanna,1 aInstitute of Neuropathology, University Hospital Münster, 48149 Muenster, Germany; bDepartment of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, 48149 Muenster, Germany; cINSERM, U1127, F-75013 Paris, France; dCNRS, UMR 7225, F-75013 Paris, France; eSorbonne Universités, Université Pierre et Marie Curie Paris 06, UM-75, F-75005 Paris, France; fInstitut du Cerveau et de la Moelle epinière-Groupe Hospitalier Pitié-Salpêtrière, F-75013 Paris, France; gMontreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4; hInstitute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany; iDepartment of Physiology, McGill University, Montreal, QC, Canada H3A 2B4; jDFG Research Center for Regenerative Therapies, Technische Universität Dresden, 01307 Dresden, Germany; kMedical Faculty, Department of Anatomy and Molecular Embryology, Ruhr-University Bochum, 44801 Bochum, Germany; and lMedicial Faculty, Westphalian Wilhelms-University of Muenster, 48149 Muenster, Germany Edited by Brigid L. M. Hogan, Duke University Medical Center, Durham, NC, and approved February 1, 2017 (received for review August 30, 2016) Rapid and efficient protocols to generate oligodendrocytes (OL) more, these protocols require long culture periods (70–150 d) to + from human induced pluripotent stem cells (iPSC) are currently obtain O4 OL and show limited efficiency (9–12). -
Neurotransmitter Release Is Blocked Intracellularly by Botulinum
Proc. Nati. Acad. Sci. USA Vol. 85, pp. 4090-4094, June 1988 Neurobiology Neurotransmitter release is blocked intracellularly by botulinum neurotoxin, and this requires uptake of both toxin polypeptides by a process mediated by the larger chain (cholinergic synapse/noncholinergic synapse/intraceliular in ection/quantal release/Aplysia cahifornica) B. POULAIN*, L. TAUC*, E. A. MAISEYt, J. D. F. WADSWORTHt, P. M. MOHANt, AND J. 0. DOLLYt *Laboratoire de Neurobiologie Cellulaire et Moldculaire, Centre National de la Recherche Scientifique, Gif-sur-Yvette, 91190 France; tDepartment of Biochemistry, Imperial College, London SW7 2AZ, United Kingdom; and tDepartment of Zoology, S.V. University, Tirupati, 515502, A.P. India Communicated by C. Ladd Prosser, February 17, 1988 ABSTRACT Botulinum neurotoxins (types A and B), lacking for a multistep process of intoxication (2, 9) and, in which are microbial proteins consisting of two disulfide-linked particular, for the inactivation by BoNT of an intracellular chains, inhibit specifically and with high potency the release of component that is involved in the release of AcCho and, acetylcholine from peripheral nerve terminals. As a prerequi- perhaps, all transmitters. Furthermore, the role of the HC site for a long-term development of effective treatments for and LC of BoNT in such a scheme for its neuroparalytic botulism, the internalization and inhibitory action of the toxin action remains to be established. Pertinent questions to be and its constituent chains were examined by electrophysiolog- answered include (i) whether the HC, by virtue of its ability ical methods at identified synapses inAplysia preparations that to recognize neural membrane receptors (8, 10), allows allow both intracellular and bath application of the neurotox- functional recognition ofcholinergic terminals and whether it ins. -
Scripting NEURON
Scripting NEURON Robert A. McDougal Yale School of Medicine 7 August 2018 What is a script? A script is a file with computer-readable instructions for performing a task. In NEURON, scripts can: set-up a model, define and perform an experimental protocol, record data, . Why write scripts for NEURON? Automation ensures consistency and reduces manual effort. Facilitates comparing the suitability of different models. Facilitates repeated experiments on the same model with different parameters (e.g. drug dosages). Facilitates recollecting data after change in experimental protocol. Provides a complete, reproducible version of the experimental protocol. Programmer's Reference neuron.yale.edu Use the \Switch to HOC" link in the upper-right corner of every page if you need documentation for HOC, NEURON's original programming language. HOC may be used in combination with Python: use h.load file to load a HOC library; the functions and classes are then available with an h. prefix. Introduction to Python Displaying results The print command is used to display non-graphical results. It can display fixed text: print ('Hello everyone.') Hello everyone. or the results of a calculation: print (5 * (3 + 2)) 25 Storing results Give values a name to be able to use them later. a = max([1.2, 5.2, 1.7, 3.6]) print (a) 5.2 In Python 2.x, print is a keyword and the parentheses are unnecessary. Using the parentheses allows your code to work with both Python 2.x and 3.x. Don't repeat yourself Lists and for loops To do the same thing to several items, put the items in a list and use a for loop: numbers = [1, 3, 5, 7, 9] for number in numbers: print (number * number) 1 9 25 49 81 Items can be accessed directly using the [] notation; e.g. -
NEST by Example: an Introduction to the Neural Simulation Tool NEST Version 2.6.0
NEST by Example: An Introduction to the Neural Simulation Tool NEST Version 2.6.0 Marc-Oliver Gewaltig1, Abigail Morrison2, and Hans Ekkehard Plesser3, 2 1Blue Brain Project, Ecole Polytechnique Federale de Lausanne, QI-J, Lausanne 1015, Switzerland 2Institute of Neuroscience and Medicine (INM-6) Functional Neural Circuits Group, J¨ulich Research Center, 52425 J¨ulich, Germany 3Dept of Mathematical Sciences and Technology, Norwegian University of Life Sciences, PO Box 5003, 1432 Aas, Norway Abstract The neural simulation tool NEST can simulate small to very large networks of point-neurons or neurons with a few compartments. In this chapter, we show by example how models are programmed and simulated in NEST. This document is based on a preprint version of Gewaltig et al. (2012) and has been updated for NEST 2.6 (r11744). Updated to 2.6.0 Hans E. Plesser, December 2014 Updated to 2.4.0 Hans E. Plesser, June 2014 Updated to 2.2.2 Hans E. Plesser & Marc-Oliver Gewaltig, December 2012 1 Introduction NEST is a simulator for networks of point neurons, that is, neuron models that collapse the morphol- ogy (geometry) of dendrites, axons, and somata into either a single compartment or a small number of compartments (Gewaltig and Diesmann, 2007). This simplification is useful for questions about the dynamics of large neuronal networks with complex connectivity. In this text, we give a practical intro- duction to neural simulations with NEST. We describe how network models are defined and simulated, how simulations can be run in parallel, using multiple cores or computer clusters, and how parts of a model can be randomized. -
9.01 Introduction to Neuroscience Fall 2007
MIT OpenCourseWare http://ocw.mit.edu 9.01 Introduction to Neuroscience Fall 2007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 9.01 Recitation (R02) RECITATION #2: Tuesday, September 18th Review of Lectures: 3, 4 Reading: Chapters 3, 4 or Neuroscience: Exploring the Brain (3rd edition) Outline of Recitation: I. Previous Recitation: a. Questions on practice exam questions from last recitation? II. Review of Material: a. Exploiting Axoplasmic Transport b. Types of Glia c. THE RESTING MEMBRANE POTENTIAL d. THE ACTION POTENTIAL III. Practice Exam Questions IV. Questions on Pset? Exploiting Axoplasmic Transport: Maps connections of the brain Rates of transport: - slow: - fast: Examples: Uses anterograde transport: - Uses retrograde transport: - - - Types of Glia: - Microglia: - Astrocytes: - Myelinating Glia: 1 THE RESTING MEMBRANE POTENTIAL: The Cast of Chemicals: The Movement of Ions: Influences by two factors: (1) Diffusion: (2) Electricity: Ohm’s Law: I = gV Ionic Equilibrium Potentials (EION): + Example: ENs * diffusional and electrical forces are equal 2 Nernst Equation: EION = 2.303 RT/zF log [ion]0/[ion]i Calculates equilibrium potential for a SINGLE ion. Inside Outside EION (at 37°C) + [K ] + [Na ] 2+ [Ca ] [Cl ] *Pumps maintain concentration gradients (ex. sodiumpotassium pump; calcium pump) Resting Membrane Potential (VM at rest): Measured resting membrane potential: 65 mV Goldman Equation: + + + + VM = 61.54 mV log (PK[K ]o + PNa[Na ]o)/ (PK[K ]I + PNa[Na ]i) + + Calculates membrane potential when permeable to both Na and K . Remember: at REST, gK >>> gNa therefore, VM is closer to EK THE ACTION POTENTIAL (Nerve Impulse): Phases of an Action Potential: Vm (mV) ENa 0 EK Time 3 Conductance of Ion Channels during AP: Remember: Changes in conductance, or permeability of the membrane to a specific ion, changes the membrane potential. -
NEURON and Python
ORIGINAL RESEARCH ARTICLE published: 28 January 2009 NEUROINFORMATICS doi: 10.3389/neuro.11.001.2009 NEURON and Python Michael L. Hines1, Andrew P. Davison2* and Eilif Muller3 1 Computer Science, Yale University, New Haven, CT, USA 2 Unité de Neurosciences Intégratives et Computationelles, CNRS, Gif sur Yvette, France 3 Laboratory for Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne, Switzerland Edited by: The NEURON simulation program now allows Python to be used, alone or in combination with Rolf Kötter, Radboud University, NEURON’s traditional Hoc interpreter. Adding Python to NEURON has the immediate benefi t Nijmegen, The Netherlands of making available a very extensive suite of analysis tools written for engineering and science. Reviewed by: Felix Schürmann, Ecole Polytechnique It also catalyzes NEURON software development by offering users a modern programming Fédérale de Lausanne, Switzerland tool that is recognized for its fl exibility and power to create and maintain complex programs. At Volker Steuber, University of the same time, nothing is lost because all existing models written in Hoc, including graphical Hertfordshire, UK user interface tools, continue to work without change and are also available within the Python Arnd Roth, University College London, UK context. An example of the benefi ts of Python availability is the use of the xml module in *Correspondence: implementing NEURON’s Import3D and CellBuild tools to read MorphML and NeuroML model Andrew Davison, UNIC, Bât. 32/33, specifi cations. CNRS, 1 Avenue de la Terrasse, 91198 Keywords: Python, simulation environment, computational neuroscience Gif sur Yvette, France. e-mail: [email protected] INTRODUCTION for the purely numerical issue of how many compartments are The NEURON simulation environment has become widely used used to represent each of the cable sections.