D
Dan F. M. Goodman
Researcher at Imperial College London
Publications - 71
Citations - 4326
Dan F. M. Goodman is an academic researcher from Imperial College London. The author has contributed to research in topics: Spiking neural network & Python (programming language). The author has an hindex of 23, co-authored 69 publications receiving 3372 citations. Previous affiliations of Dan F. M. Goodman include Paris Descartes University & École Normale Supérieure.
Papers
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Journal ArticleDOI
Spike sorting for large, dense electrode arrays
Cyrille Rossant,Cyrille Rossant,Shabnam Kadir,Shabnam Kadir,Dan F. M. Goodman,John Schulman,Maximilian L. D. Hunter,Maximilian L. D. Hunter,Aman B. Saleem,Andres Grosmark,Mariano Belluscio,George H. Denfield,Alexander S. Ecker,Andreas S. Tolias,Samuel G. Solomon,György Buzsáki,Matteo Carandini,Kenneth D. Harris,Kenneth D. Harris +18 more
TL;DR: A set of tools to solve the problem of decoding the spike times of the recorded neurons from the raw data captured from the probes, implemented in a suite of practical, user-friendly, open-source software is presented.
Journal ArticleDOI
The brian simulator.
TL;DR: “Brian” is a simulator for spiking neural networks that uses vector-based computation to allow for efficient simulations, and is particularly useful for neuroscientific modelling at the systems level, and for teaching computational neuroscience.
Journal ArticleDOI
Brian: A Simulator for Spiking Neural Networks in Python
Dan F. M. Goodman,Romain Brette +1 more
TL;DR: A new simulator for spiking neural networks, written in Python, which will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations.
Journal ArticleDOI
Brian: a simulator for spiking neural networks in Python
Dan F. M. Goodman,Romain Brette +1 more
TL;DR: “Brian” is a new simulator for spiking neural networks, written in Python, which will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations.
Journal ArticleDOI
Brian 2, an intuitive and efficient neural simulator
TL;DR: Brian 2 allows scientists to simply and efficiently simulate spiking neural network models by transforming code with simple and concise high-level descriptions into efficient low-level code that can run interleaved with their code.