Computing with Spiking Neuron Networks
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Spiking Neural Networks: Asurvey Oscar T
Oscar T. Skaug Spiking Neural Networks: A Survey Bachelor’s project in Computer engineering Supervisor: Ole Christian Eidheim June 2020 Bachelor’s project NTNU Engineering Faculty of Information Technology and Electrical Faculty of Information Technology Norwegian University of Science and Technology Abstract The purpose of the survey is to support teaching and research in the department of Computer Science at the university, through fundamental research in recent advances in the machine learning field. The survey assumes the reader have some basic knowledge in machine learning, however, no prior knowledge on the topic of spiking neural networks is assumed. The aim of the survey is to introduce the reader to artificial spiking neural networks by going through the following: what constitutes and distinguish an artificial spiking neural network from other artificial neural networks; how to interpret the output signals of spiking neural networks; How supervised and unsupervised learning can be achieved in spiking neural networks; Some benefits of spiking neural networks compared to other artificial neural networks, followed by challenges specifically associated with spiking neural networks. By doing so the survey attempts to answer the question of why spiking neural networks have not been widely implemented, despite being both more computationally powerful and biologically plausible than other artificial neural networks currently available. Norwegian University of Science and Technology Trondheim, June 2020 Oscar T. Skaug The bachelor thesis The title of the bachelor thesis is “Study of recent advances in artificial and biological neuron models” with the purpose of “supporting teaching and research in the department of Computer Science at the university through fundamental research in recent advances in the machine learning field”. -
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. -
Simplified Spiking Neural Network Architecture and STDP Learning
Iakymchuk et al. EURASIP Journal on Image and Video Processing (2015) 2015:4 DOI 10.1186/s13640-015-0059-4 RESEARCH Open Access Simplified spiking neural network architecture and STDP learning algorithm applied to image classification Taras Iakymchuk*, Alfredo Rosado-Muñoz, Juan F Guerrero-Martínez, Manuel Bataller-Mompeán and Jose V Francés-Víllora Abstract Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning. -
Spiking Neural Network Vs Multilayer Perceptron: Who Is the Winner in the Racing Car Computer Game
Soft Comput (2015) 19:3465–3478 DOI 10.1007/s00500-014-1515-2 FOCUS Spiking neural network vs multilayer perceptron: who is the winner in the racing car computer game Urszula Markowska-Kaczmar · Mateusz Koldowski Published online: 3 December 2014 © The Author(s) 2014. This article is published with open access at Springerlink.com Abstract The paper presents two neural based controllers controller on-line. The RBF network is used to establish a for the computer car racing game. The controllers represent nonlinear prediction model. In the recent paper (Zheng et al. two generations of neural networks—a multilayer perceptron 2013) the impact of heavy vehicles on lane-changing deci- and a spiking neural network. They are trained by an evolu- sions of following car drivers has been investigated, and the tionary algorithm. Efficiency of both approaches is experi- lane-changing model based on two neural network models is mentally tested and statistically analyzed. proposed. Computer games can be perceived as a virtual representa- Keywords Computer game controller · Spiking neural tion of real world problems, and they provide rich test beds network · Multilayer perceptron · Evolutionary algorithm for many artificial intelligence methods. An example which can be mentioned here is successful application of multi- layer perceptron in racing car computer games, described for 1 Introduction instance in Ebner and Tiede (2009) and Togelius and Lucas (2005). Inspiration for our research were the results shown in Neural networks (NN) are widely applied in many areas Yee and Teo (2013) presenting spiking neural network based because of their ability to learn. controller for racing car in computer game. -
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..