Modeling Spiking Neural Networks on SpiNNaker

By Xin Jin, Mikel Luján, Luis A. Plana, Sergio Davies, Steve Temple, S. Furber. 2010

98 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…implement aspects of such models (e.g., Fieres, Schemmel, & Meier, 2008; Glackin, McGinnity, Maguire, Wu, & Belatreche, 2005; Indiveri et al., 2011; Jin et al., 2010; Khan et al., 2008; Liu et al., 2001; Merolla et al., 2014; Neil & Liu, 2014; Roggen, Hofmann, Thoma, & Floreano, 2003; Schemmel,…”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “This could benefit the field of neuromorphic engineering [74–76], which develops dedicated massively parallel and very efficient hardware for emulating spiking neural networks and suitable plasticity rules.”
Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. (2013, methodology): “Despite the fast, on-pass learning in the deSNN models, in terms of large scale modelling of millions and billions of neurons using the SpiNNaker SNN supercomputer system (Jin et al., 2010) for simulation purposes would be appropriate, especially at the level of parameter optimisation.”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “This approach, on one hand, leads to the implementation of compact and low-power behaving systems ranging from brain-machine interfaces to autonomous robotic agents.”
Neuromorphic Silicon Neuron Circuits (2011, methodology): “Examples include solutions using FPGAs (Mak et al., 2006; Cassidy and Andreou, 2008), multi-core based architectures using multiple ARM cores (Jin et al., 2010), and conventional graphical processing units (GPUs; Fidjeland et al., 2009).”
Overview of the SpiNNaker System Architecture (2013, background): “, [2], [3]; a complete list of SpiNNaker publications is available on the project web site [1]), the contribution here is to offer a comprehensive overview focusing on the motivation and rationale for the architectural decisions taken in the design of the machine.”
Learning in spiking neural networks (2013, background): “• Xin Jin, Mikel Lujan, Luis A. Plana, Sergio Davies, Steve Temple and Steve Furber “Modelling Spiking Neural Networks on SpiNNaker” published in Computing in Science & Engineering, 2010, volume 12, number 5, pages 91–97 (Jin et al., 2010b);”