A Continuous-Time Spiking Neural Network Paradigm

1. Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997)

   Article  Google Scholar 

2. Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in design and application of spiking neural networks. Soft Computing - A Fusion of Foundations, Methodologies and Applications 11(3), 239–248 (2006)

   MathSciNet  Google Scholar 

3. Ponulak, F., Kasiński, A.: Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiol. Exp. 71(4), 409–433 (2011)

   Google Scholar 

4. Brunel, N., van Rossum, M.C.W.: Lapicque’s 1907 paper: from frogs to integrate-and-fire. Biol. Cybern. 97(5-6), 337–339 (2007)

   Article  MATH  Google Scholar 

5. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)

   Article  Google Scholar 

6. Izhikevich, E.M.: Which Model to Use for Cortical Spiking Neurons? IEEE Trans. on Neural Networks 15(5), 1063–1070 (2004)

   Article  Google Scholar 

7. Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Comput. 18(2), 245–282 (2006)

   Article  MATH  MathSciNet  Google Scholar 

8. Chrol-Cannon, J., Gruning, A., Yaochu, J.: The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2012)

   Google Scholar 

9. Edelman, G.M.: Neural Darwinism: The Theory of Neuronal Group Selection. Basic Book, Inc., New York (1987)

   Google Scholar 

10. Izhikevich, E.M., Gally, J.A., Edelman, G.M.: Spike-timing Dynamics of Neuronal Groups. Cerebral Cortex 14(8), 933–944 (2004)

    Article  Google Scholar 

11. Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95(1), 1–19 (2006)

    Article  MATH  MathSciNet  Google Scholar 

12. Burkitt, A.N.: A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties. Biol. Cybern. 95(2), 97–112 (2006)

    Article  MATH  MathSciNet  Google Scholar 

13. Brette, R., Rudolph, M., Carnevale, T., Hines, H., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris Jr., F.C., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A.P., El Boustani, S., Destexhe, A.: Simulation of networks of spiking neurons: A review of tools and strategies. J. Comput. Neurosci. 23(3), 349–398 (2007)

    Article  MathSciNet  Google Scholar 

14. Citri, A., Malenka, R.C.: Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33(1), 18–41 (2008)

    Article  Google Scholar 

15. FitzHugh, R.: Mathematical models of threshold phenomena in the nerve membrane. Bull. Math. Biophys. 17(4), 257–278 (1955)

    Article  Google Scholar 

16. Chua, L., Yang, L.: Cellular Neural Networks: Theory. IEEE Trans. on Circuits and Systems 35(10), 1257–1272 (1988)

    Article  MATH  MathSciNet  Google Scholar 

17. Mattia, M., Del Giudice, P.: Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Comput. 12(10), 2305–2329 (2000)

    Article  Google Scholar 

18. NEURON simulator, http://www.neuron.yale.edu/neuron/

19. Wang, H., Chen, Y., Chen, Y.: First-spike latency in Hodgkin’s three classes of neurons. J. of Theoretical Biology 328, 19–25 (2013)

    Article  Google Scholar 

20. Okun, M., Lampl, I.: Balance of excitation and inhibition. Scholarpedia 4(8), 7467 (2009), http://www.scholarpedia.org/article/Balance_of_excitation_and_inhibition

    Article  Google Scholar 

21. Pernice, V., Staude, B., Cardanobile, S., Rotter, S.: Recurrent interactions in spiking networks with arbitrary topology. Physical Review E 85, 031916 (2012)

    Article  Google Scholar 

22. Buzsáki, G.: Rhythem of the brain. Oxford University Press, Inc. 198 Madison Avenue, New York (2006)

    Book  Google Scholar 

23. Parasuraman, K., Elshorbagy, A., Carey, S.: Spiking modular neural networks: a neural network modeling approach for hydrological processes. Water Resources Research 42(5), 1–14 (2006)

    Article  Google Scholar 

24. Wu, Q.X., McGinnity, M., Maguire, L., Cai, R., Chen, M.: Simulation of Visual Attention Using Hierarchical Spiking Neural Networks. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS, vol. 6840, pp. 26–31. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

25. Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393(1), 440–442 (1998)

    Article  Google Scholar 

26. Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)

    Article  MATH  MathSciNet  Google Scholar 

27. Finkel, L.H., Edelman, G.M.: Interaction of synaptic modification rules within populations of neurons. Proc. Natl. Acad. Sci. USA, 1291–1295 (1985)

    Google Scholar 

28. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature 3(9), 919–926 (2000)

    Google Scholar 

29. Sullivan, T.J., de Sa, V.R.: Homeostatic synaptic scaling in self-organizing maps. Neural Networks 19, 734–743 (2006)

    Article  MATH  Google Scholar 

30. Ros, E., Carrillo, R., Ortigosa, E.M., Barbour, B., Agís, R.: Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup tables to Characterize Neuronal Dynamics. Neural Comput 18(12), 2959–2993 (2006)

    Article  MATH  Google Scholar 

31. D’Haene, M., Schrauwen, B., Van Campenhout, J., Stroobandt, D.: Accelerating Event-Driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants. Neural Comput. 21(4), 1068–1099 (2009)

    Article  MATH  MathSciNet  Google Scholar 

Download references