Code generation in computational neuroscience: A review of tools and techniques

File Description SizeFormat 
fninf-12-00068.pdfPublished version4.54 MBAdobe PDFDownload
Title: Code generation in computational neuroscience: A review of tools and techniques
Author(s): Blundell, I
Brette, R
Cleland, TA
Close, TG
Coca, D
Davison, AP
Diaz-Pier, S
Musoles, CF
Gleeson, P
Goodman, DFM
Hines, M
Hopkins, MW
Kumbhar, P
Lester, DR
Marin, B
Morrison, A
Mueller, E
Nowotny, T
Peyser, A
Plotnikov, D
Richmond, P
Rowley, A
Rumpe, B
Stimberg, M
Stokes, AB
Tomkins, A
Trensch, G
Woodman, M
Eppler, JM
Item Type: Journal Article
Abstract: Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.
Publication Date: 5-Nov-2018
Date of Acceptance: 12-Sep-2018
URI: http://hdl.handle.net/10044/1/64579
DOI: https://dx.doi.org/10.3389/fninf.2018.00068
ISSN: 1662-5196
Publisher: Frontiers Media
Journal / Book Title: Frontiers in Neuroinformatics
Volume: 12
Copyright Statement: © 2018 Blundell, Brette, Cleland, Close, Coca, Davison, Diaz-Pier, Fernandez Musoles, Gleeson, Goodman, Hines, Hopkins, Kumbhar, Lester, Marin, Morrison, Müller, Nowotny, Peyser, Plotnikov, Richmond, Rowley, Rumpe, Stimberg, Stokes, Tomkins, Trensch, Woodman and Eppler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology
Life Sciences & Biomedicine
Mathematical & Computational Biology
Neurosciences
Neurosciences & Neurology
code generation
simulation
neuronal networks
domain specific language
modeling language
SPIKING NEURONS
NEURAL MODELS
NETWORK
SIMULATIONS
SPECIFICATION
ENVIRONMENT
INTERFACE
LANGUAGE
SOLVERS
Science & Technology
Life Sciences & Biomedicine
Mathematical & Computational Biology
Neurosciences
Neurosciences & Neurology
code generation
simulation
neuronal networks
domain specific language
modeling language
SPIKING NEURONS
NEURAL MODELS
NETWORK
SIMULATIONS
SPECIFICATION
ENVIRONMENT
INTERFACE
LANGUAGE
SOLVERS
Publication Status: Published
Article Number: 68
Open Access location: https://www.frontiersin.org/articles/10.3389/fninf.2018.00068/full
Online Publication Date: 2018-11-05
Appears in Collections:Electrical and Electronic Engineering



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons