PyNN: A Common Interface for Neuronal Network Simulators.
Andrew P. Davison,Daniel Brüderle,Jochen Martin Eppler,Jens Kremkow,Eilif Muller,Dejan Pecevski,Laurent Perrinet,Pierre Yger +7 more
TLDR
PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools.Abstract:
Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization, and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.read more
Citations
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Journal ArticleDOI
BrainNet Viewer: a network visualization tool for human brain connectomics.
Mingrui Xia,Jinhui Wang,Yong He +2 more
TL;DR: This work has developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models, and helps researchers to visualize brain networks in an easy, flexible and quick manner.
Journal ArticleDOI
The SpiNNaker Project
TL;DR: SpiNNaker as discussed by the authors is a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time.
Proceedings Article
The SpiNNaker project
TL;DR: The current state of the spiking neural network architecture project is reviewed, and the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world is presented.
Journal ArticleDOI
Overview of the SpiNNaker System Architecture
Steve Furber,David Lester,Luis A. Plana,Jim Garside,Eustace Painkras,Steve Temple,Andrew Brown +6 more
TL;DR: Three of the principal axioms of parallel machine design (memory coherence, synchronicity, and determinism) have been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations.
Posted Content
A Survey of Neuromorphic Computing and Neural Networks in Hardware.
Catherine D. Schuman,Thomas E. Potok,Robert M. Patton,J. Douglas Birdwell,Mark Edward Dean,Garrett S. Rose,James S. Plank +6 more
TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
References
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Journal ArticleDOI
Simple model of spiking neurons
TL;DR: A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons and combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons.
Journal ArticleDOI
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Book
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TL;DR: Covering details of NEURON's inner workings, and practical considerations specifying anatomical and biophysical properties to be represented in models, this book uses a problem-solving approach that includes many examples to challenge readers.
Journal ArticleDOI
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
Romain Brette,Wulfram Gerstner +1 more
TL;DR: The authors' simple model predicts correctly the timing of 96% of the spikes of the detailed model in response to injection of noisy synaptic conductances and has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.
Journal ArticleDOI
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