DOCSLIB.ORG

Arbor – a Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Arbor – a Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures Arbor – a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures Nora Abi Akar Ben Cumming Vasileios Karakasis Scientific Software & Libraries Scientific Software & Libraries Scientific Computing Support CSCS CSCS CSCS Zurich,¨ Switzerland Zurich,¨ Switzerland Lugano, Switzerland [email protected] [email protected] [email protected] Anne Kusters¨ Wouter Klijn Alexander Peyser SimLab Neuroscience, JSC, IAS SimLab Neuroscience, JSC, IAS SimLab Neuroscience, JSC, IAS Forschungszentrum Jlich Forschungszentrum Jlich Forschungszentrum Jlich Julich,¨ Germany Julich,¨ Germany Julich,¨ Germany [email protected] [email protected] [email protected] Stuart Yates Scientific Software & Libraries CSCS Zurich,¨ Switzerland [email protected] Abstract—We introduce Arbor, a performance portable library models handling complex, bifurcating dendritic arbors [2] for simulation of large networks of multi-compartment neurons and assuming binary axonal firing and velocity allowed for on HPC systems. Arbor is open source software, developed under the mathematical construction of entire neurons. With the the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, addition of synaptic models in the ensuing decades, the and NVIDIA GPUs. When coupled with low memory overheads, fundamental formal description of biological neuronal networks these optimizations make Arbor an order of magnitude faster was captured at the scale of cellular behavior. than the most widely-used comparable simulation software. The The evolution of computing equipment ranging from the single-node performance can be scaled out to run very large desktop PC to supercomputing centers has enabled a plethora models at extreme scale with efficient weak scaling. Index Terms—HPC, GPU, neuroscience, neuron, software of tools for numerically computing predictions of neuronal network behavior that is comparable with a variety of experi- I. INTRODUCTION mental results, thus allowing the rigorous testing of possible functional models with varying levels of experimental verifi- “What I cannot create, I do not understand.” cation, mathematical validity and stability, and computational – Feynmann’s blackboard, February 1988 performance. In 1984, the publication of the Thomas algorithm arXiv:1901.07454v1 [q-bio.NC] 17 Jan 2019 The electrical basis of neuronal activity was experimentally for neurons and its implementation in CABLE and NEURON established in 1902 by Bernstein [1, Review] with preliminary were the computational breakthrough for efficient computations chemical models of ionic electrodiffusion. Further refinement of complex dendritic arbors and the diffusion of computational by Hodgkin, Huxley, Katz and Stmpfli of the connection simulation to the wider neuroscientific community [3]. Contin- between ionic flow, electrical activity, and axonal firing quickly uing improvements to NEURON and the addition of automated followed in the wet lab with the classical series of experiments code generation for neuron models to allow the injection of on squid giant axons. However, the crucial breakthrough efficient C code by neuroscientists not versed in programming in understanding the link between biological activity and made NEURON the premier neuronal network simulator for electrical activity in neuronal networks was the theoretical multicompartment neurons. description by Hodgkin and Huxley in 1952 in mathematical However, the field of neuronal network simulators is diverse, and engineering language which allowed the computation of encompassing point neuron simulators like NEST, interpreted the depolarization and repolarization of neurons from a few simulators for fast prototyping like BRIAN, and high-resolution experimentally accessible parameters. Full multicompartment simulators like GENESIS [4, Review]. As the field expands its ambitions from small neuronal networks towards the 1011 neurons in a human brain, and beyond by a further two orders of magnitude to include electrically active glial cells, issues of J1 J2 parallel computational performance have begun to dominate the field [4], featuring experimental simulators like SPLIT. Both NEURON and NEST have been ported to MPI for large clusters and high-performance computing (HPC) centers; Jmembrane petascale-capable code has been developed as CoreNEURON, NEST 4G and Arbor. Use cases have included the neocortical Fig. 1: Current balance in dendrite section, Jmembrane = J2 − J1. The simulations [5], macaque visual cortical areas [6], and olfactory membrane is treated as a leaky capacitor with extra current sources, surrounding an ohmic conductor of variable radius. bulb simulations [7]. New HPC architectures such as the addition of ubiquitous GPU resources have been a new challenge, requiring new code adaptation with codes such as CoreNEURON and GeNN for single-node GPU neuronal networks. Developing performant densities, derived from: the membrane capacitance; a surface algorithms for computing the Hines matrix on GPUs and other density of ion channels; and point sources from synapses and vectorize hardware has been an additional hurdle [8], [9]. The experimentally-injected currents. The synapse and ion channel development of Arbor [10] has focused on tackling issues of currents themselves are functions of local states that are in vectorization and emerging hardware architectures by using turn governed by a system of voltage-dependent ODEs. modern C++ and automated code generation, within an open- source and open-development model. The resulting equations have the form II. MODEL Arbor simulates networks of spiking neurons, and in partic- ular, networks of multi-compartment neurons. @ @v @v X rev @S In this model, the only interaction between cells is mediated σ = cm · + gk(s (x; t))(v − e ) · @x @x @t k k @x by spikes1, discrete events initiated by one neuron which are channels k then propagated and distributed to typically many other neurons X syn syn syn syn + Ii (sk (t); v(xk )) δxk after a delay. This abstracts the process by which an action synapses k X potential is generated by a neuron and then carried via an axon + Iinj(t) δxinj; to a number of remote synapses. k k injections k For simulation purposes, this interaction between neurons is (1a) modeled as a tuple: source neuron, destination neuron, synapse d on the destination neuron, propagation time, and a weight. The s (x; t) = f (s ; v(x; t)); (1b) dt k i k strictly positive propagation times allow for semi-independent d evolution of neuron state: if ∆T is the minimum propagation ssyn(t) = f syn(ssyn; v(xsyn; t); t); (1c) dt k i k k time, then the integration of the neuron state from time t to t + ∆T=2 only needs spikes that were generated at t < t − ∆T=2. While Arbor supports a number of neuron models, the main focus is on the simulation of multi-compartment neurons. Here where σ is the axial conductivity of the intracellular medium; each cell is modeled as a branching, one dimensional electrical cm is the membrane areal capacitance; gk is the areal con- system with dynamics governed by the cable equation, with ductance for an ion channel of type k as a function of an ion channel state sk, with corresponding reversal potential ion channels and synapses represented by additional current rev ek ; S(x) is the membrane surface area as a function of sources. (See [11] for an early example of this model applied to syn axial distance x; Ik is the current produced by a synapse at a crustacean axon.) The domain is discretized (hence the term syn syn position xk as a function of the synaptic state sk and local multi-compartment) and the consequent system of differential inj inj voltage; and Ik (t) is the injected current at position xk. The equations is integrated numerically. Spikes are generated when syn the membrane voltage at a given point — typically on the functions fk and fk encode the biophysical properties of the soma — exceeds a fixed threshold. ion channels and synapses, and will vary from simulation to The cable equation is derived from the balance of trans- simulation. membrane currents with the axial currents that travel through Arbor performs a vertex-centered 1-D finite volume dis- the intracellular medium (Fig. 1). Membrane currents com- cretization in space, using a first-order approximation for the prise a mixture of discrete currents and distributed current axial current flux. This provides a coupled set of ODEs for the 1Interaction via gap junctions is under development in Arbor, but is not discretized voltages Vi for each control volume Xi, the synapse syn currently supported. states sk , and the discretized states of the ion channels sk;i in Xi: cell implementations, while target-specific vectorization, code- generation and cell group implementations allow hardware- dVi X X c = σ (V − V ) − Iinj(t) i dt i;j j i k optimized performance of models specified in a portable and inj j: Xj \Xi=6 ; k: xk 2Xi generic way. X syn syn − Ik (sk ;Vi) (2a) A. Cells and recipes k: xsyn2X k i The basic unit of abstraction in an Arbor model is the cell. X rev − Si · gk(sk;i)(Vi − ek ); A cell represents the smallest model that can be simulated, channels k and forms the smallest unit of work that can be distributed ds across processes. Cells interact with each other only via spike k;i = f (s ;V ); (2b) dt k k;i i exchange. dssyn Cells can be of various types, admitting different representa- k = f syn(ssyn;V ; t); (2c) dt k k i tions and implementations. Arbor
Load more

Recommended publications
  • 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 .
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • GENESIS: a System for Simulating Neural Networks 487
    485 GENESIS: A SYSTEM FOR SIMULATING NEURAL NETWOfl.KS Matthew A. Wilson, Upinder S. Bhalla, John D. Uhley, James M. Bower. Division of Biology California Institute of Technology Pasadena, CA 91125 ABSTRACT We have developed a graphically oriented, general purpose simulation system to facilitate the modeling of neural networks. The simulator is implemented under UNIX and X-windows and is designed to support simulations at many levels of detail. Specifically, it is intended for use in both applied network modeling and in the simulation of detailed, realistic, biologically­ based models. Examples of current models developed under this system include mammalian olfactory bulb and cortex, invertebrate central pattern generators, as well as more abstract connectionist simulations. INTRODUCTION Recently, there has been a dramatic increase in interest in exploring the computational properties of networks of parallel distributed processing elements (Rumelhart and McClelland, 1986) often referred to as Itneural networks" (Anderson, 1988). Much of the current research involves numerical simulations of these types of networks (Anderson, 1988; Touretzky, 1989). Over the last several years, there has also been a significant increase in interest in using similar computer simulation techniques to study the structure and function of biological neural networks. This effort can be seen as an attempt to reverse-engineer the brain with the objective of understanding the functional organization of its very complicated networks (Bower, 1989). Simulations of these systems range from detailed reconstructions of single neurons, or even components of single neurons, to simulations of large networks of complex neurons (Koch and Segev, 1989). Modelers associated with each area of research are likely to benefit from exposure to a large range of neural network simulations.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Computing with Spiking Neuron Networks
    Computing with Spiking Neuron Networks Hel´ ene` Paugam-Moisy1 and Sander Bohte2 Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the his- tory of the “spiking neuron” in Section 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Section 2. The computational power of SNNs is addressed in Section 3 and the problem of learning in networks of spiking neurons is tackled in Section 4, with insights into the tracks currently explored for solving it. Finally, Section 5 discusses application domains, implementation issues and proposes several simulation frameworks. 1 Professor at Universit de Lyon Laboratoire de Recherche en Informatique - INRIA - CNRS bat. 490, Universit Paris-Sud Orsay cedex, France e-mail: [email protected] 2 CWI Amsterdam, The Netherlands e-mail: [email protected] 1 Contents Computing with Spiking Neuron Networks :::::::::::::::::::::::::: 1 Hel´ ene` Paugam-Moisy1 and Sander Bohte2 1 From natural computing to artificial neural networks .
    [Show full text]
  • Neuron2020day3a.Pdf • a Script Is a File with Computer-Readable Instructions for Performing a Task
    from neuron import h from neuron.units import mV, ms from matplotlib import cm import plotly h.load_file('stdrun.hoc') h.load_file('c91662.ses') h.hh.insert(h.allsec()) ic = h.IClamp(h.soma(0.5)) ic.delay = 1 * ms ic.dur = 1 * ms ic.amp = 10 h.finitialize(-65 * mV) h.continuerun(2 * ms) Scripting NEURON ps = h.PlotShape(False) ps.variable('v') Robert A. McDougal ps.plot(plotly, cmap=cm.cool).show() 23 November 2020 The slides are available at… neuron.yale.edu/ftp/neuron/neuron2020/NEURON2020day3a.pdf • A script is a file with computer-readable instructions for performing a task. What is a • In NEURON, scripts can: • set-up a module script? • define and perform an experimental protocol • record data • save and load data • and more … • Automation ensures consistency and reduces manual effort. • Facilitates comparing the suitability of different models. Why write • Facilitates repeated experiments on the scripts for same model with different parameters NEURON? (e.g. drug dosages). • Facilitates re-collecting data after change in experimental protocol. • Provides a complete, reproducible version of the experimental protocol. 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. There are many Python distributions. Any should work, but many people prefer Anaconda as it comes with a large set of useful libraries. Introduction to Python Displaying results: the print function Variables • Give things a name to access them later: Lists and for loops • To do the same thing to several items, put the items in a list and use a for loop: • Items in a list can be accessed directly using the [] notation.
    [Show full text]