SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons

References

• Hines M L, Carnevale N T. The NEURON simulation environment. Neural Comput. 1997; 9: 1179–209
   PubMed Web of Science ®Google Scholar
• Bower J M, Beeman D. The Book of GENESIS: Exploring Realistic Neural Models with the General SImulation System2nd edn. Springer, New York 1998
   Google Scholar
• Watts L. Event-driven simulation of networks of spiking neurons. Advances in Neural Information Processing Systems. Morgan Kaufmann, San Mateo, CA 1994; 6: 927–34
   Google Scholar
• Delorme A, Gautrais J, VanRullen R, Thorpe S J. SpikeNET: a simulator for modelling large networks of integrate and fire neurons. Neurocomputing 1999; 26/27: 989–96
   Web of Science ®Google Scholar
• Grassmann C, Anlauf J K. Fast digital simulation of spiking neural networks and neuromorphic integration with SPIKELAB. Int. J. Neural Syst. 1999; 9: 473–8
   PubMedGoogle Scholar
• Mattia M, Del Guidice P. Efficient event-driven simmulation of large networks of spiking neurons and dynamical synapses. Neural Comput. 2000; 12: 2305–29
   PubMed Web of Science ®Google Scholar
• Thorpe S, Gautrais J. Rapid visual processing using spike asynchrony. Neural Information Processing System 1996, M J Michael, P Thomas. MIT Press, Cambridge, MA 1997; 9: 901–8
   Google Scholar
• Lapicque L. Recherches quantitatives sur l'éxitation électrique des nerfs traté comme une polarisation. J. Physiol. Pathol. Gen. 1907; 9: 620–35
   Google Scholar
• Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation nerve. J. Physiol. (Lond.) 1952; 117: 500–44
   Google Scholar
• Gerstner W. Population dynamics of spiking neurons: fast transients, asynchronous states, and locking. Neural Comput. 2000; 12: 43–89
   PubMed Web of Science ®Google Scholar
• Hansel D, Mato G, Meunier C, Neltner L. On numerical simulations of integrate-and-fire neural networks. Neural Comput. 1998; 10: 467–83
   PubMed Web of Science ®Google Scholar
• Destexhe A. Conductance-based integrate-and-fire models. Neural Comput. 1997; 9: 503–14
   PubMed Web of Science ®Google Scholar
• Kistler W M, Gerstner W, van Hemmen J. Reduction of the Hodgkin–Huxley equations to a single-variable threshold model. Neural Comput. 1997; 9: 1015–45
   Web of Science ®Google Scholar
• Jaffe D B, Carnevale N T. Passive normalization of synaptic integration influenced by dendritic architecture. J. Neurophysiol. 1999; 82: 3268–85
   PubMed Web of Science ®Google Scholar
• Reich D S, Victor J D, Knight B W. The power ratio and the interval map: spiking models and extracellular recordings. J. Neurosci. 1998; 18: 10090–104
   PubMed Web of Science ®Google Scholar
• Goddard N, Hood G. Parallel genesis for large scale modelling. Computational Neuroscience: Trends in Research 1997. Plenum, New York 1997; 911–17
   Google Scholar
• Goddard N, Hood G, Howell F, Hines M, De Schutter E. NEOSIM: portable large.scale plug and play modelling. Neurocomputing 2001; 38: 1657–61
   Web of Science ®Google Scholar
• Markram H, Lubke J, Frotscher M, Sakmann B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 1997; 275: 213–15
   PubMed Web of Science ®Google Scholar
• Delorme A, Perinnet L, Thorpe S. Network of integrate-and-fire neurons using rank order coding B: spike timing dependant plasticity and emergence of orientation selectivity. Neurocomputing 2001; 38–40: 539–45
   Web of Science ®Google Scholar
• Delorme A, Van Rullen R, Thorpe S J. Rapid object recognition based on asynchronous feed-forward processing. 22nd European Conf. on Visual Perception, TriesteItaly, 1999; 28: 128-9, Perception, (Suppl.)
   Google Scholar
• Van Rullen R, Gautrais J, Delorme A, Thorpe S. Face processing using one spike per neurone. Biosystems 1998; 48: 229–39
   PubMed Web of Science ®Google Scholar
• Paquier W, Delorme A, Thorpe S. Motion processing using one spike per neuron. Computational Neuroscience: Trends in Research 2001 (9th Annual Computational Neuroscience Mtg, CNS'00, Brugge, July 2000, J M Bower. Elsevier, Amsterdam 2000
   Google Scholar
• Delorme A, Thorpe S. Face recognition using one spike per neuron: resistance to image degradation. Neural Networks 2001; 14: 795–803
   PubMed Web of Science ®Google Scholar
• Liley D T, Alexander D M, Wright J J, Aldous M D. Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons. Network: Comput. Neural Syst. 1999; 10: 79–92
   PubMed Web of Science ®Google Scholar
• Golomb D, Ermentrout G B. Continuous and lurching traveling pulses in neuronal networks with delay and spatially decaying connectivity. Proc. Natl Acad. Sci. USA 1999; 96: 13480–5
   PubMed Web of Science ®Google Scholar
• Samsonovich A, McNaughton B L. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 1997; 17: 5900–20
   PubMed Web of Science ®Google Scholar
• Hopfield J J, Herz A V. Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons. Proc. Natl Acad. Sci. USA 1995; 92: 6655–62
   PubMed Web of Science ®Google Scholar
• Reich D S, Victor J D, Knight B W, Ozaki T, Kaplan E. Response variability and timing precision of neuronal spike trains in vivo. J. Neurophysiol. 1997; 77: 2836–41
   PubMed Web of Science ®Google Scholar
• Thorpe S J. Spike arrival times : a highly efficient coding scheme for neural networks. Parallel Processing in Neural Systems, R Eckmiller, G Hartman, G Hauske. Elsevier, Amsterdam 1990
   Google Scholar
• Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses. Neural Comput. 1998; 10: 821–35
   PubMed Web of Science ®Google Scholar
• De Schutter E. A consumer guide to neuronal modelling software. Trends Neurosci. 1992; 15: 462–4
   Web of Science ®Google Scholar
• Delorme A, Thorpe S. SpikeNET web site, http://www.sccn.ucsd.edu/∼arno/spikenet
   Google Scholar