GENESIS: a System for Simulating Neural Networks 487
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
UNIX and Computer Science Spreading UNIX Around the World: by Ronda Hauben an Interview with John Lions
Winter/Spring 1994 Celebrating 25 Years of UNIX Volume 6 No 1 "I believe all significant software movements start at the grassroots level. UNIX, after all, was not developed by the President of AT&T." Kouichi Kishida, UNIX Review, Feb., 1987 UNIX and Computer Science Spreading UNIX Around the World: by Ronda Hauben An Interview with John Lions [Editor's Note: This year, 1994, is the 25th anniversary of the [Editor's Note: Looking through some magazines in a local invention of UNIX in 1969 at Bell Labs. The following is university library, I came upon back issues of UNIX Review from a "Work In Progress" introduced at the USENIX from the mid 1980's. In these issues were articles by or inter- Summer 1993 Conference in Cincinnati, Ohio. This article is views with several of the pioneers who developed UNIX. As intended as a contribution to a discussion about the sig- part of my research for a paper about the history and devel- nificance of the UNIX breakthrough and the lessons to be opment of the early days of UNIX, I felt it would be helpful learned from it for making the next step forward.] to be able to ask some of these pioneers additional questions The Multics collaboration (1964-1968) had been created to based on the events and developments described in the UNIX "show that general-purpose, multiuser, timesharing systems Review Interviews. were viable." Based on the results of research gained at MIT Following is an interview conducted via E-mail with John using the MIT Compatible Time-Sharing System (CTSS), Lions, who wrote A Commentary on the UNIX Operating AT&T and GE agreed to work with MIT to build a "new System describing Version 6 UNIX to accompany the "UNIX hardware, a new operating system, a new file system, and a Operating System Source Code Level 6" for the students in new user interface." Though the project proceeded slowly his operating systems class at the University of New South and it took years to develop Multics, Doug Comer, a Profes- Wales in Australia. -
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. -
Evidence for Opposing Roles of Celsr3 and Vangl2 in Glutamatergic Synapse Formation
Evidence for opposing roles of Celsr3 and Vangl2 in glutamatergic synapse formation Sonal Thakara, Liqing Wanga, Ting Yua, Mao Yea, Keisuke Onishia, John Scotta, Jiaxuan Qia, Catarina Fernandesa, Xuemei Hanb, John R. Yates IIIb, Darwin K. Berga, and Yimin Zoua,1 aNeurobiology Section, Biological Sciences Division, University of California, San Diego, La Jolla, CA 92093; and bDepartment of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037 Edited by Liqun Luo, Stanford University, Stanford, CA, and approved December 5, 2016 (received for review July 22, 2016) The signaling mechanisms that choreograph the assembly of the conditionally knocking out these components in defined synapses highly asymmetric pre- and postsynaptic structures are still poorly after the development of axons and dendrites. defined. Using synaptosome fractionation, immunostaining, and In this study, we directly address the role of Celsr3 and Vangl2 coimmunoprecipitation, we found that Celsr3 and Vangl2, core in glutamatergic synapse formation by deleting Celsr3 and Vangl2 components of the planar cell polarity (PCP) pathway, are localized in hippocampal pyramidal neurons after the first postnatal week at developing glutamatergic synapses and interact with key synap- (postnatal day 7; P7). We found that at the peak of synapse for- tic proteins. Pyramidal neurons from the hippocampus of Celsr3 mation (P14), Celsr3 and Vangl2 are specifically localized in de- knockout mice exhibit loss of ∼50% of glutamatergic synapses, veloping glutamatergic synapses and colocalized with pre- and but not inhibitory synapses, in culture. Wnts are known regulators postsynaptic proteins. In the absence of Celsr3,hippocampal of synapse formation, and our data reveal that Wnt5a inhibits glu- neurons showed ∼50% reduction in the number of glutamatergic tamatergic synapses formed via Celsr3. -
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. -
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. -
NEURAL CONNECTIONS: Some You Use, Some You Lose
NEURAL CONNECTIONS: Some You Use, Some You Lose by JOHN T. BRUER SOURCE: Phi Delta Kappan 81 no4 264-77 D 1999 . The magazine publisher is the copyright holder of this article and it is reproduced with permission. Further reproduction of this article in violation of the copyright is prohibited JOHN T. BRUER is president of the James S. McDonnell Foundation, St. Louis. This article is adapted from his new book, The Myth of the First Three Years (Free Press, 1999), and is reprinted by arrangement with The Free Press, a division of Simon Schuster Inc. ©1999, John T. Bruer . OVER 20 YEARS AGO, neuroscientists discovered that humans and other animals experience a rapid increase in brain connectivity -- an exuberant burst of synapse formation -- early in development. They have studied this process most carefully in the brain's outer layer, or cortex, which is essentially our gray matter. In these studies, neuroscientists have documented that over our life spans the number of synapses per unit area or unit volume of cortical tissue changes, as does the number of synapses per neuron. Neuroscientists refer to the number of synapses per unit of cortical tissue as the brain's synaptic density. Over our lifetimes, our brain's synaptic density changes in an interesting, patterned way. This pattern of synaptic change and what it might mean is the first neurobiological strand of the Myth of the First Three Years. (The second strand of the Myth deals with the notion of critical periods, and the third takes up the matter of enriched, or complex, environments.) Popular discussions of the new brain science trade heavily on what happens to synapses during infancy and childhood. -
A Brief History of Unix
A Brief History of Unix Tom Ryder [email protected] https://sanctum.geek.nz/ I Love Unix ∴ I Love Linux ● When I started using Linux, I was impressed because of the ethics behind it. ● I loved the idea that an operating system could be both free to customise, and free of charge. – Being a cash-strapped student helped a lot, too. ● As my experience grew, I came to appreciate the design behind it. ● And the design is UNIX. ● Linux isn’t a perfect Unix, but it has all the really important bits. What do we actually mean? ● We’re referring to the Unix family of operating systems. – Unix from Bell Labs (Research Unix) – GNU/Linux – Berkeley Software Distribution (BSD) Unix – Mac OS X – Minix (Intel loves it) – ...and many more Warning signs: 1/2 If your operating system shows many of the following symptoms, it may be a Unix: – Multi-user, multi-tasking – Hierarchical filesystem, with a single root – Devices represented as files – Streams of text everywhere as a user interface – “Formatless” files ● Data is just data: streams of bytes saved in sequence ● There isn’t a “text file” attribute, for example Warning signs: 2/2 – Bourne-style shell with a “pipe”: ● $ program1 | program2 – “Shebangs” specifying file interpreters: ● #!/bin/sh – C programming language baked in everywhere – Classic programs: sh(1), awk(1), grep(1), sed(1) – Users with beards, long hair, glasses, and very strong opinions... Nobody saw it coming! “The number of Unix installations has grown to 10, with more expected.” — Ken Thompson and Dennis Ritchie (1972) ● Unix in some flavour is in servers, desktops, embedded software (including Intel’s management engine), mobile phones, network equipment, single-board computers.. -
The Action Potential
See discussions, stats, and author profiles for this publication at: http://www.researchgate.net/publication/6316219 The action potential ARTICLE in PRACTICAL NEUROLOGY · JULY 2007 Source: PubMed CITATIONS READS 16 64 2 AUTHORS, INCLUDING: Mark W Barnett The University of Edinburgh 21 PUBLICATIONS 661 CITATIONS SEE PROFILE Available from: Mark W Barnett Retrieved on: 24 October 2015 192 Practical Neurology HOW TO UNDERSTAND IT Pract Neurol 2007; 7: 192–197 The action potential Mark W Barnett, Philip M Larkman t is over 60 years since Hodgkin and called ion channels that form the permeation Huxley1 made the first direct recording of pathways across the neuronal membrane. the electrical changes across the neuro- Although the first electrophysiological nal membrane that mediate the action recordings from individual ion channels were I 2 potential. Using an electrode placed inside a not made until the mid 1970s, Hodgkin and squid giant axon they were able to measure a Huxley predicted many of the properties now transmembrane potential of around 260 mV known to be key components of their inside relative to outside, under resting function: ion selectivity, the electrical basis conditions (this is called the resting mem- of voltage-sensitivity and, importantly, a brane potential). The action potential is a mechanism for quickly closing down the transient (,1 millisecond) reversal in the permeability pathways to ensure that the polarity of this transmembrane potential action potential only moves along the axon in which then moves from its point -
Scripting NEURON with Python
Scripting NEURON with Python Robert A. McDougal Yale School of Medicine 11 November 2016 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. 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. n = number[2] To check if an item is in a list, use in: print (4 in [3, 1, 4, 1, 5, 9]) True print (7 in [3, 1, 4, 1, 5, 9]) False Dictionaries If there is no natural order, specify your own keys using a dictionary. -
Modeling in Python-NEURON
Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information Modeling in Python-NEURON Michael Hines Yale School of Medicine School of Brain Cells and Circuits; Erice 2015 Special thanks to Robert McDougal Program development supported by NINDS These slides available at: http://neuron.yale.edu/ftp/neuron/neuron-python-erice2015.pdf Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information What is a script What is a script? A script is a file with computer-readable instructions for performing a task. Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information Why write scripts for NEURON? 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. Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information Why Python? Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information Why Python... Why Python?... Very large community. Huge number of modules useful in CNS. Graphics. Vector, Matrix computation. Databases. Connections to the rest of the world. Why write scripts? Why Python? Introduction to Python Basic NEURON scripting Advanced topics More information Why Python... ...Why Python? Far greater elegance and expressiveness than Hoc.