Spikenet

SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons. Many biological neural network models face the problem of scalability because of the limited computational power of today’s computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modelled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (<0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.


References in zbMATH (referenced in 12 articles )

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  1. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  2. Caron, Louis-Charles; D’Haene, Michiel; Mailhot, Frédéric; Schrauwen, Benjamin; Rouat, Jean: Event management for large scale event-driven digital hardware spiking neural networks (2013)
  3. Lallee, S.; Yoshida, E.; Mallet, A.; Nori, F.; Natale, L.; Metta, G.; Warneken, F.; Dominey, P.F.: Human-robot cooperation based on interaction learning (2010)
  4. Yalamanchili, Pavan; Mohan, Sumod; Jalasutram, Rommel; Taha, Tarek: Acceleration of hierarchical Bayesian network based cortical models on multicore architectures (2010)
  5. Plesser, Hans E.; Diesmann, Markus: Simplicity and efficiency of integrate-and-fire neuron models (2009)
  6. Stewart, Robert D.; Bair, Wyeth: Spiking neural network simulation: numerical integration with the Parker-Sochacki method (2009)
  7. Brette, Romain; Rudolph, Michelle; Carnevale, Ted; Hines, Michael; Beeman, David; Bower, James M.; Diesmann, Markus; Morrison, Abigail; Goodman, Philip H.; Harris, Frederick C.Jr.; Zirpe, Milind; Natschläger, Thomas; Pecevski, Dejan; Ermentrout, Bard; Djurfeldt, Mikael; Lansner, Anders; Rochel, Olivier; Vieville, Thierry; Muller, Eilif; Davison, Andrew P.; El Boustani, Sami; Destexhe, Alain: Simulation of networks of spiking neurons: A review of tools and strategies (2007)
  8. Johansson, Christopher; Lansner, Anders: Towards cortex sized artificial neural systems (2007)
  9. Migliore, M.; Cannia, C.; Lytton, W.W.; Markram, Henry; Hines, M.L.: Parallel network simulations with NEURON (2006)
  10. Migliore, M.; Cannia, C.; Lytton, W.W.; Markram, Henry; Hines, M.L.: Parallel network simulations with NEURON (2006)
  11. Morrison, Abigail; Mehring, Carsten; Geisel, Theo; Aertsen, Ad; Diesmann, Markus: Advancing the boundaries of high-connectivity network simulation with distributed computing (2005)
  12. O’Dwyer, Carl; Richardson, Daniel: Spiking neural nets with symbolic internal state (2005)