SpiNNaker is a novel computer architecture inspired by the working of the human brain. A SpiNNaker machine is a massively parallel computing platform, targeted towards three main areas of research: Neuroscience: Understanding how the brain works is a Grand Challenge of 21st century science. We will provide the platform to help neuroscientists to unravel the mystery that is the mind. The largest SpiNNaker machine will be capable of simulating a billion simple neurons, or millions of neurons with complex structure and internal dynamics. Robotics: SpiNNaker is a good target for researchers in robotics, who need mobile, low power computation. A small SpiNNaker board makes it possible to simulate a network of tens of thousands of spiking neurons, process sensory input and generate motor output, all in real time and in a low power system. Computer Science: SpiNNaker breaks the rules followed by traditional supercomputers that rely on deterministic, repeatable communications and reliable computation. SpiNNaker nodes communicate using simple messages (spikes) that are inherently unreliable. This break with determinism offers new challenges, but also the potential to discover powerful new principles of massively parallel computation

References in zbMATH (referenced in 31 articles )

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  1. Hussain, Khaled F.; Bassyouni, Mohamed Yousef; Gelenbe, Erol: Accurate, energy-efficient classification with spiking random neural network (2021)
  2. Stöckel, Andreas; Eliasmith, Chris: Passive nonlinear dendritic interactions as a computational resource in spiking neural networks (2021)
  3. Yin, Yonghua: Random neural network methods and deep learning (2021)
  4. Fil, Jakub; Chu, Dominique: Minimal spiking neuron for solving multilabel classification tasks (2020)
  5. Huyck, Christian Robert; Vergani, Alberto Arturo: Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons (2020)
  6. Vich, C.; Dunovan, K.; Verstynen, Timothy; Rubin, J.: Corticostriatal synaptic weight evolution in a two-alternative forced choice task: a computational study (2020)
  7. Collins, Logan T.: The case for emulating insect brains using anatomical “wiring diagrams” equipped with biophysical models of neuronal activity (2019)
  8. Diamond, Alan; Schmuker, Michael; Nowotny, Thomas: An unsupervised neuromorphic clustering algorithm (2019)
  9. Fitzsimmons, M.; Kunze, H.: Combining Hopfield neural networks, with applications to grid-based mathematics puzzles (2019)
  10. Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi: GXNOR-Net: training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework (2018)
  11. Mostafa, Hesham; Cauwenberghs, Gert: A learning framework for winner-take-all networks with stochastic synapses (2018)
  12. Verzi, Stephen J.; Rothganger, Fredrick; Parekh, Ojas D.; Quach, Tu-Thach; Miner, Nadine E.; Vineyard, Craig M.; James, Conrad D.; Aimone, James B.: Computing with spikes: the advantage of fine-grained timing (2018)
  13. Voelker, Aaron R.; Eliasmith, Chris: Improving spiking dynamical networks: accurate delays, higher-order synapses, and time cells (2018)
  14. Kelly, Matthew A.; Mewhort, D. J. K.; West, Robert L.: The memory tesseract: mathematical equivalence between composite and separate storage memory models (2017)
  15. Hopkins, Michael; Furber, Steve: Accuracy and efficiency in fixed-point neural ODE solvers (2015)
  16. Lagorce, Xavier; Benosman, Ryad: STICK: spike time interval computational kernel, a framework for general purpose computation using neurons, precise timing, delays, and synchrony (2015)
  17. Schmidhuber, Jürgen: Deep learning in neural networks: an overview (2015) ioport
  18. Adams, Samantha V.; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F.: Adaptive training of cortical feature maps for a robot sensorimotor controller (2013) ioport
  19. Denk, Christian; Llobet-Blandino, Francisco; Galluppi, Francesco; Plana, Luis A.; Furber, Steve; Conradt, Jörg: Real-time interface board for closed-loop robotic tasks on the spinnaker neural computing system (2013) ioport
  20. Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition (2013) ioport

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Further publications can be found at: http://apt.cs.manchester.ac.uk/projects/SpiNNaker/Publications/