Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Andrew P Davison"'
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 8, p e1012342 (2024)
Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of
Externí odkaz:
https://doaj.org/article/642a657acf1c4b8aad436f629787ada3
Autor:
Andrew P Davison, Shailesh Appukuttan
Publikováno v:
eLife, Vol 11 (2022)
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.
Externí odkaz:
https://doaj.org/article/bee230c750a94dadb75b44a5ee362b5b
Autor:
Sára Sáray, Christian A Rössert, Shailesh Appukuttan, Rosanna Migliore, Paola Vitale, Carmen A Lupascu, Luca L Bologna, Werner Van Geit, Armando Romani, Andrew P Davison, Eilif Muller, Tamás F Freund, Szabolcs Káli
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 1, p e1008114 (2021)
Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and elect
Externí odkaz:
https://doaj.org/article/9f7f926fcfaa4b02965089d1a930f72c
Autor:
Kael Dai, Juan Hernando, Yazan N Billeh, Sergey L Gratiy, Judit Planas, Andrew P Davison, Salvador Dura-Bernal, Padraig Gleeson, Adrien Devresse, Benjamin K Dichter, Michael Gevaert, James G King, Werner A H Van Geit, Arseny V Povolotsky, Eilif Muller, Jean-Denis Courcol, Anton Arkhipov
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 2, p e1007696 (2020)
Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simu
Externí odkaz:
https://doaj.org/article/96cd62f444274b59b61eee3ed7ac4d6c
Autor:
Eilif eMuller, James A Bednar, Markus eDiesmann, Marc-Oliver eGewaltig, Michael eHines, Andrew P Davison
Publikováno v:
Frontiers in Neuroinformatics, Vol 9 (2015)
Externí odkaz:
https://doaj.org/article/eee08e05d404421386580a7e1caab517
Autor:
Samuel eGarcia, Domenico eGuarino, Florent eJaillet, Todd R Jennings, Robert ePröpper, Philipp L Rautenberg, Chris eRodgers, Andrey eSobolev, Thomas eWachtler, Pierre eYger, Andrew P Davison
Publikováno v:
Frontiers in Neuroinformatics, Vol 8 (2014)
Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific produ
Externí odkaz:
https://doaj.org/article/5898ec4bb9b146f8b361ab8b139b675e
Autor:
Ján eAntolík, Andrew P Davison
Publikováno v:
Frontiers in Neuroinformatics, Vol 7 (2013)
The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental p
Externí odkaz:
https://doaj.org/article/fa86a2bb54cc42f4a8380620a8206d5e
Autor:
Padraig Gleeson, Sharon Crook, Robert C Cannon, Michael L Hines, Guy O Billings, Matteo Farinella, Thomas M Morse, Andrew P Davison, Subhasis Ray, Upinder S Bhalla, Simon R Barnes, Yoana D Dimitrova, R Angus Silver
Publikováno v:
PLoS Computational Biology, Vol 6, Iss 6, p e1000815 (2010)
Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MO
Externí odkaz:
https://doaj.org/article/d6213c4969c9433db88533de3998fec6
Publikováno v:
Frontiers in Neuroscience, Vol 3 (2009)
Neuroscience simulators allow scientists to express models in terms of biological concepts, without having to concern themselves with low-level computational details of their implementation. The expressiveness, power and ease-of-use of the simulator
Externí odkaz:
https://doaj.org/article/08fe6503699047b49a351a8849daedab
Autor:
Daniel Brüderle, Eric Müller, Andrew P Davison, Eilif Muller, Johannes Schemmel, Karlheinz Meier
Publikováno v:
Frontiers in Neuroinformatics, Vol 3 (2009)
Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. Howeve
Externí odkaz:
https://doaj.org/article/2460a5cfe9dc43fba4fea5f0c69b3a96