Andrew Davison

Documents

Publications

	NeoViewer: Facilitating reuse of electrophysiology data through browser-based interactive visualization Onur Ates , Shailesh Appukuttan , Hélissande Fragnaud , Corentin Fragnaud , Andrew P. Davison SoftwareX, 2024, 26, pp.101710. ⟨10.1016/j.softx.2024.101710⟩ Journal articles hal-04551480v1
	EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience Shailesh Appukuttan , Luca L. Bologna , Felix Schürmann , Michele Migliore , Andrew P. Davison Neuroinformatics, 2023, 21 (1), pp.101-113. ⟨10.1007/s12021-022-09598-z⟩ Journal articles hal-03783756v1
	The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models Luca Leonardo Bologna , Roberto Smiriglia , Carmen Alina Lupascu , Shailesh Appukuttan , Andrew P. Davison Frontiers in Neuroinformatics, 2022, 16, pp.991609. ⟨10.3389/fninf.2022.991609⟩ Journal articles hal-03788861v1
	A faster way to model neuronal circuitry Andrew P. Davison , Shailesh Appukuttan eLife, 2022, 11, pp.e84463. ⟨10.7554/eLife.84463⟩ Journal articles hal-04304568v1
	HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data Sára Sáray , Christian A Rössert , Shailesh Appukuttan , Rosanna Migliore , Paola Vitale PLoS Computational Biology, 2021, 17 (1), pp.e1008114. ⟨10.1371/journal.pcbi.1008114⟩ Journal articles hal-03063383v1
	The SONATA data format for efficient description of large-scale network models Kael Dai , Juan Hernando , Yazan Billeh , Sergey Gratiy , Judit Planas PLoS Computational Biology, 2020, 16 (2), pp.e1007696. ⟨10.1371/journal.pcbi.1007696⟩ Journal articles hal-02913116v1
	[Rp] Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model Andrew P. Davison ReScience C, 2020, ⟨10.5281/zenodo.3972130⟩ Journal articles hal-03046451v1
	Editorial: Reproducibility and Rigour in Computational Neuroscience Sharon Crook , Andrew P. Davison , Robert Mcdougal , Hans Ekkehard Plesser Frontiers in Neuroinformatics, 2020, 14, pp.23. ⟨10.3389/fninf.2020.00023⟩ Journal articles hal-02913122v1
	Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits Padraig Gleeson , Matteo Cantarelli , Boris Marin , Adrian Quintana , Matt Earnshaw Neuron, 2019, 103 (3), pp.395. ⟨10.1016/j.neuron.2019.05.019⟩ Journal articles hal-02193259v1
	Arkheia: Data Management and Communication for Open Computational Neuroscience Ján Antolík , Andrew P. Davison Frontiers in Neuroinformatics, 2018, 12, pp.6. ⟨10.3389/fninf.2018.00006⟩ Journal articles hal-01737956v1
	Code Generation in Computational Neuroscience: A Review of Tools and Techniques Inga Blundell , Romain Brette , Thomas A Cleland , Thomas G Close , Daniel Coca Frontiers in Neuroinformatics, 2018, 12, pp.68. ⟨10.3389/fninf.2018.00068⟩ Journal articles hal-01996352v1
	Sustainable computational science: the ReScience initiative Nicolas P. Rougier , Konrad Hinsen , Frédéric Alexandre , Thomas Arildsen , Lorena Barba PeerJ Computer Science, 2017, 3, pp.e142. ⟨10.7717/peerj-cs.142⟩ Journal articles hal-01592078v1
	26th Annual Computational Neuroscience Meeting (CNS2017): Part 2 Leonid L. Rubchinsky , Sungwoo Ahn , Wouter Klijn , Ben Cumming , Stuart Yates BMC Neuroscience, 2017, 18 (Suppl 1), pp.59. ⟨10.1186/s12868-017-0371-2⟩ Journal articles hal-01580190v1*
	Toward standard practices for sharing computer code and programs in neuroscience Stephen Eglen , Ben Marwick , Yaroslav Halchenko , Michael Hanke , Shoaib Sufi Nature Neuroscience, 2017, 20 (6), pp.770-773. ⟨10.1038/nn.4550⟩ Journal articles hal-02064847v1
	A Commitment to Open Source in Neuroscience Padraig Gleeson , Andrew P. Davison , R Angus Silver , Giorgio Ascoli Neuron, 2017, 96 (5), pp.964-965. ⟨10.1016/j.neuron.2017.10.013⟩ Journal articles hal-02064834v1
	La recherche reproductible : une communication scientifique explicite Christophe Pouzat , Andrew P. Davison , Konrad Hinsen Statistique et Société, 2016, Deux débats sur les données, 3 (1) Journal articles hal-01478360v1
	Python in neuroscience. Eilif Muller , James A Bednar , Markus Diesmann , Marc-Oliver Gewaltig , Michael Hines Frontiers in Neuroinformatics, 2015, 9, pp.11. ⟨10.3389/fninf.2015.00011⟩ Journal articles hal-01159440v1
	libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience. Michael Vella , Robert C Cannon , Sharon M. Crook , Andrew P. Davison , Gautham Ganapathy Front Neuroinform, 2014, 8, pp.38. ⟨10.3389/fninf.2014.00038⟩ Journal articles hal-01055565v1
	Neo: an object model for handling electrophysiology data in multiple formats. Samuel Garcia , Domenico Guarino , Florent Jaillet , Todd Jennings , Robert Pröpper Frontiers in Neuroinformatics, 2014, 8, pp.10. ⟨10.3389/fninf.2014.00010⟩ Journal articles hal-01055571v1
	Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Mikael Djurfeldt , Andrew P. Davison , Jochen M Eppler Front Neuroinform, 2014, 8, pp.43. ⟨10.3389/fninf.2014.00043⟩ Journal articles hal-01055563v1
	Integrated workflows for spiking neuronal network simulations. Ján Antolík , Andrew P. Davison Front Neuroinform, 2013, 7, pp.34. ⟨10.3389/fninf.2013.00034⟩ Journal articles hal-01055572v1
	Automated Capture of Experiment Context for Easier Reproducibility in Computational Research Andrew P. Davison Computing in Science and Engineering, 2012, 14 (4), pp.48-56. ⟨10.1109/MCSE.2012.41⟩ Journal articles hal-01055702v1
	Creating, documenting and sharing network models. Sharon M. Crook , James A Bednar , Sandra Berger , Robert Cannon , Andrew P. Davison Network: Computation in Neural Systems, 2012, 23 (4), pp.131-49. ⟨10.3109/0954898X.2012.722743⟩ Journal articles hal-01055577v1
	Collaborative modelling: the future of computational neuroscience? Andrew P. Davison Network: Computation in Neural Systems, 2012, 23 (4), pp.157-66. ⟨10.3109/0954898X.2012.718482⟩ Journal articles hal-01055579v1
	A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Daniel Brüderle , Mihai A Petrovici , Bernhard Vogginger , Matthias Ehrlich , Thomas Pfeil Biological Cybernetics (Modeling), 2011, 104 (4-5), pp.263-96. ⟨10.1007/s00422-011-0435-9⟩ Journal articles hal-00635967v1
	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. Padraig Gleeson , Sharon M. Crook , Robert C Cannon , Michael L Hines , Guy O Billings PLoS Computational Biology, 2010, 6 (6), pp.e1000815. ⟨10.1371/journal.pcbi.1000815⟩ Journal articles hal-00586776v1
	Trends in programming languages for neuroscience simulations. Andrew P. Davison , Michael L Hines , Eilif Muller Frontiers in Neuroscience, 2009, 3 (3), pp.374-80. ⟨10.3389/neuro.01.036.2009⟩ Journal articles hal-00586787v1
	Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System Daniel Brüderle , Eric Muller , Andrew Davison , Eilif Muller , Johannes Schemmel Frontiers in Neuroinformatics, 2009, 3, pp.17. ⟨10.3389/neuro.11.017.2009⟩ Journal articles hal-03047041v1
	PyNN: A Common Interface for Neuronal Network Simulators. Andrew P. Davison , Daniel Brüderle , Jochen Eppler , Jens Kremkow , Eilif Muller Frontiers in Neuroinformatics, 2009, 2, pp.11. ⟨10.3389/neuro.11.011.2008⟩ Journal articles hal-00586786v1
	Reliable recall of spontaneous activity patterns in cortical networks. Olivier Marre , Pierre Yger , Andrew P. Davison , Yves Frégnac Journal of Neuroscience, 2009, 29 (46), pp.14596-606. ⟨10.1523/JNEUROSCI.0753-09.2009⟩ Journal articles hal-00444943v1
	NEURON and Python. Michael L Hines , Andrew P. Davison , Eilif Muller Front Neuroinformatics, 2009, 3, pp.1. ⟨10.3389/neuro.11.001.2009⟩ Journal articles hal-00586782v1
	Simulation of networks of spiking neurons: A review of tools and strategies. Romain Brette , Michael Rudolph , Ted Carnevale , Michael Hines , David Beeman Journal of Computational Neuroscience, 2007, 23 (3), pp.349-98. ⟨10.1007/s10827-007-0038-6⟩ Journal articles hal-00180662v1
	Learning cross-modal spatial transformations through spike timing-dependent plasticity. Andrew P. Davison , Yves Frégnac Journal of Neuroscience, 2006, 26 (21), pp.5604-15. ⟨10.1523/JNEUROSCI.5263-05.2006⟩ Journal articles hal-00120625v1
	Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Mathilde Badoual , Quan Zou , Andrew P. Davison , Michael Rudolph , Thierry Bal International Journal of Neural Systems, 2006, 16 (2), pp.79-97 Journal articles hal-00120626v1
	Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation. Andrew P. Davison , Thomas M Morse , Michele Migliore , Gordon M Shepherd , Michael L Hines Neuroinformatics, 2004, 2 (3), pp.327-32. ⟨10.1385/NI:2:3:327⟩ Journal articles hal-00299855v1
	ModelDB: making models publicly accessible to support computational neuroscience. Michele Migliore , Thomas M Morse , Andrew P. Davison , Luis Marenco , Gordon M Shepherd Neuroinformatics, 2003, 1 (1), pp.135-9. ⟨10.1385/NI:1:1:135⟩ Journal articles hal-00586793v1
	Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model. Andrew P. Davison , Jianfeng Feng , David Brown Journal of Neurophysiology, 2003, 90 (3), pp.1921-35. ⟨10.1152/jn.00623.2002⟩ Journal articles hal-00299879v1
	A reduced compartmental model of the mitral cell for use in network models of the olfactory bulb. Andrew P. Davison , J. Feng , D. Brown Brain Research Bulletin, 2000, 51 (5), pp.393-9 Journal articles hal-00635986v1

	Helmholtz: a customizable framework for neurophysiology data management Andrew P. Davison , Thierry Bal , Domenico Guarino , Olivier Manette , Cyril Monier Neuroinformatics 2013, INCF, Stockholm, Sweden, Aug 2013, Stockholm, Sweden. ⟨10.3389/conf.fninf.2013.09.00025⟩ Conference papers hal-03047325v1
	Reliable recall of spontaneous activity patterns in chaotic cortical networks Olivier Marre , Pierre Yger , Andrew Davison , Yves Frégnac Deuxième conférence française de Neurosciences Computationnelles, "Neurocomp08", Oct 2008, Marseille, France Conference papers hal-00331545v1
	STDP in topogra-phical neuronal networks. Pierre Yger , Abigail Morrison , M. Diesmann , Andrew P. Davison , Yves Frégnac Proceedings of the 44th Japan Workshop - ISTC-RIKEN BSI, 2008, Wako, Japan. pp.en cours Conference papers hal-00382367v1
	PyNN: Towards a universal neural simulator API in Python. Andrew P. Davison , Pierre Yger , Jens Kremkow , Laurent Perrinet , Eilif Muller CNS 2007 Proceedings of the 16th Annual Computational Neuroscience Meeting, 2007, Toronto, Canada. pp.73 Conference papers hal-00381688v1
	Plasticité associative dépendante de l'ordre temporel entre activités pré- et post-synaptiques (Spike Timing Dependent Plasticity) : son implication dans l'adaptation fonctionnelle des réseaux sensoriels et l'apprentissage perceptif. Mathilde Badoual , Thierry Bal , Pierre Baudot , Andrew P. Davison , Alain Destexhe Colloque de l'ACI (Action Concertée Incitative) Neurosciences Intégratives et Computationnelles, 2007, Paris, France. pp.en cours Conference papers hal-00380905v1
	A combined pyschophysical--modelling investigation of the mechanisms of tactile picture perception. Andrew P. Davison , Pierre Yger , Yves Frégnac , J. Chan , F. Newell Proceedings of the 15th Annual Computational Neuroscience Meeting, 2006, Edinburgh, United Kingdom. pp.170 Conference papers hal-00380061v1
	Learning simple computations in spiking basis function networks using spike time-dependent plasticity. Andrew P. Davison 4th Forum of European Neuroscience, 2004, Lisbon, Portugal. pp.A224 Conference papers hal-00379381v1
	Training a network of spiking neurons to perform coordinate transformations. Andrew P. Davison , Yves Frégnac 34th Annual Meeting of the Society for Neuroscience, 2004, San Diego, United States. pp.4 Conference papers hal-00379386v1
	Spike synchronization in a biophysically-detailed model of the olfactory bulb Andrew P. Davison , Jf Feng , D. Brown 9th Annual Computational Neuroscience Meeting, 2000, Brugge, Belgium. pp.515-521, ⟨10.1016/S0925-2312(01)00391-5⟩ Conference papers hal-01055784v1

	Neo: a Data Model for Electrophysiological Data Representation and its Python Implementation Florent Jaillet , Samuel Garcia , Andrew P. Davison , Luc Estebanez , Pierre Yger 17th Annual Meeting of the Organization for Human Brain Mapping (HBM 2011), Jun 2011, Québec, Canada. Conference poster hal-04128682v1

	Complexity in Neuronal Networks Yves Frégnac , Michael Rudolph , Andrew P. Davison , Alain Destexhe François Képès. Biological Networks, World Scientific, pp.291-338, 2007, Complex Systems and Interdisciplinary Science vol.3, 9789812706959 Book sections hal-00739550v1
	Complexity and level hierarchy in neural networks Yves Frégnac , Michael Rudolph , Andrew P. Davison , Alain Destexhe François Képès. Biological networks, World Scientific Publishing Company, pp.1-52, 2007, Complex Systems and Interdisciplinary Science, vol. 3 Book sections hal-00211742v1
	Biologically-Detailed Network Modelling Andrew P. Davison Jianfeng Feng. Computational Neuroscience. A Comprehensive Approach, Chapman & Hall / CRC Press, pp.287-304, 2004, Chapman & Hall/CRC Mathematical Biology and Medicine Book sections hal-00377318v1

	The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps Domenico G Guarino , Andrew P. Davison , Yves Frégnac , Ján Antolík 2023 Preprints, Working Papers, ... hal-04304628v1
	Model descriptions in Neuroscience: computational performance and collaboration Pedro Garcia-Rodriguez , Andrew P. Davison , Lungsi Sharma , Shailesh Appukuttan 2022 Preprints, Working Papers, ... hal-03586671v2

	HBP Neuromorphic Computing Platform version 2021A Andrew P. Davison , Jonathan Duperrier 2021 Software hal-03330386v1
	Neo Viewer v1.4.0 Andrew P. Davison , Onur Ates , Corentin Fragnaud 2021 Software hal-03330213v1
	HBP Model Validation Framework v1.7 Shailesh Appukuttan , Andrew P. Davison 2021 Software hal-03330341v1
	PyNN v0.9.6 Andrew P. Davison , Laurent U Perrinet , Onur Ates , Eric Müller , Rémy Cagnol 2020 Software hal-03330164v1
	Neo v0.9.0 Julia Sprenger , Samuel Garcia , Alexander Kleinjohann , Hugo van Kemenade , Achileas Koutsou 2020 Software hal-03330047v1
	Neo v0.8.0 Achileas Koutsou , Chek Yin Choi , Richard C Gerkin , Hugo van Kemenade , Alexander Kleinjohann 2019 Software hal-03329305v1
	fairgraph v0.5.1 Andrew P. Davison , Glynis Mattheisen , Yann Zerlaut 2019 Software hal-03330091v1

Andrew Davison

Publications

NeoViewer: Facilitating reuse of electrophysiology data through browser-based interactive visualization

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

A faster way to model neuronal circuitry

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

The SONATA data format for efficient description of large-scale network models

[Rp] Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model

Editorial: Reproducibility and Rigour in Computational Neuroscience

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Arkheia: Data Management and Communication for Open Computational Neuroscience

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Sustainable computational science: the ReScience initiative

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Toward standard practices for sharing computer code and programs in neuroscience

A Commitment to Open Source in Neuroscience

La recherche reproductible : une communication scientifique explicite

Python in neuroscience.

libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

Neo: an object model for handling electrophysiology data in multiple formats.

Efficient generation of connectivity in neuronal networks from simulator-independent descriptions.

Integrated workflows for spiking neuronal network simulations.

Automated Capture of Experiment Context for Easier Reproducibility in Computational Research

Creating, documenting and sharing network models.

Collaborative modelling: the future of computational neuroscience?

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.

NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.

Trends in programming languages for neuroscience simulations.

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

PyNN: A Common Interface for Neuronal Network Simulators.

Reliable recall of spontaneous activity patterns in cortical networks.

NEURON and Python.

Simulation of networks of spiking neurons: A review of tools and strategies.

Learning cross-modal spatial transformations through spike timing-dependent plasticity.

Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity.

Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation.

ModelDB: making models publicly accessible to support computational neuroscience.

Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model.

A reduced compartmental model of the mitral cell for use in network models of the olfactory bulb.

Helmholtz: a customizable framework for neurophysiology data management

Reliable recall of spontaneous activity patterns in chaotic cortical networks

STDP in topogra-phical neuronal networks.

PyNN: Towards a universal neural simulator API in Python.

Plasticité associative dépendante de l'ordre temporel entre activités pré- et post-synaptiques (Spike Timing Dependent Plasticity) : son implication dans l'adaptation fonctionnelle des réseaux sensoriels et l'apprentissage perceptif.

A combined pyschophysical--modelling investigation of the mechanisms of tactile picture perception.

Learning simple computations in spiking basis function networks using spike time-dependent plasticity.

Training a network of spiking neurons to perform coordinate transformations.

Spike synchronization in a biophysically-detailed model of the olfactory bulb

Neo: a Data Model for Electrophysiological Data Representation and its Python Implementation

Complexity in Neuronal Networks

Complexity and level hierarchy in neural networks

Biologically-Detailed Network Modelling

The cortico-thalamic loop attunes competitive lateral interactions across retinotopic and orientation preference maps

Model descriptions in Neuroscience: computational performance and collaboration

HBP Neuromorphic Computing Platform version 2021A

Neo Viewer v1.4.0

HBP Model Validation Framework v1.7

PyNN v0.9.6

Neo v0.9.0

Neo v0.8.0

fairgraph v0.5.1