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Effects of Pruning on Phase-Coding and Storage Capacity of a Spiking Network | SpringerLink
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Effects of Pruning on Phase-Coding and Storage Capacity of a Spiking Network

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Recent Advances of Neural Network Models and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 26))

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Abstract

Synaptic pruning is a crucial process during development. We study the imprinting and replay of spatiotemporal patterns in a spiking network, as a function of pruning degree. After a Spike Timing Dependent Plasticity-based learning of synaptic efficacies, the weak synapses are removed through a competitive pruning process. Surprisingly, after this pruning stage, the storage capacity for spatiotemporal patterns is relatively high also for very high diluition ratio.

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References

  1. Siegel, M., Warden, M.R., Miller, E.K.: Phase-dependent neuronal coding of objects in short-term memory. PNAS 106, 21341–21346 (2009)

    Article  Google Scholar 

  2. Buzsaki, G., Draguhn, A.: Neuronal Oscillations in Cortical Networks. Science 304, 1926–1929 (2004)

    Article  Google Scholar 

  3. Kayser, C., Montemurro, M.A., Logothetis, N.K., Panzeri, S.: Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron. 61, 597–608 (2009)

    Article  Google Scholar 

  4. Gerstner, W., Ritz, R., van Hemmen, J.L.: Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biological Cybernetics 69(5-6), 503–515 (1993)

    MATH  Google Scholar 

  5. Markram, H., Lubke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997)

    Article  Google Scholar 

  6. Bi, G.Q., Poo, M.M.: Precise spike timing determines the direction and extent of synaptic modifications in cultured hippocampal neurons. J. Neurosci. 18, 10464–10472 (1998)

    Google Scholar 

  7. Scarpetta, S., Zhaoping, L., Hertz, J.: Hebbian Imprinting and Retrieval in Oscillatory Neural Networks. Neural Computation 14(10), 2371–2396 (2002)

    Article  MATH  Google Scholar 

  8. Scarpetta, S., Marinaro, M.: A learning rule for place fields in a cortical model: Theta phase precession as a network effect. Hippocampus 15(7), 979–989 (2005)

    Article  Google Scholar 

  9. Zhaoping, L., Lewis, A., Scarpetta, S.: Mathematical analysis and simulations of the neural circuit for locomotion in lampreys. Physical Review Letters 92(19), 198106 (2004)

    Article  Google Scholar 

  10. Yoshioka, M., Scarpetta, S., Marinaro, M.: Spatiotemporal learning in analog neural networks using spike-timing-dependent synaptic plasticity. Phys. Rev. E 75, 051917 (2007)

    Google Scholar 

  11. Scarpetta, S., De Candia, A., Giacco, F.: Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity. Frontiers in Synaptic Neuroscience 2 (2010)

    Google Scholar 

  12. Marinaro, M., Scarpetta, S., Yoshioka, M.: Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule. Mathematical Biosciences 207(2), 322–335 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  13. Scarpetta, S., Giacco, F., de Candia, A.: Storage capacity of phase-coded patterns in sparse neural networks. EPL (Europhysics Letters) 95(2), 28006 (2011)

    Article  Google Scholar 

  14. Scarpetta, S., De Candia, A., Giacco, F.: Dynamics and storage capacity of neural networks with small-world topology. In: Proceedings of the 2011 Conference on Neural Nets WIRN10. Frontiers in Artificial Intelligence and Applications, vol. 226 (2011) ISBN: 978-1-60750-691-1

    Google Scholar 

  15. Yoshioka, M., Scarpetta, S., Marinaro, M.: Spike-Timing-Dependent Synaptic Plasticity to Learn Spatiotemporal Patterns in Recurrent Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007, Part I. LNCS, vol. 4668, pp. 757–766. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  16. Scarpetta, S., Giacco, F.: Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks. Journal of Computational Neuroscience. J Comput Neurosci. 34(2), 319–336 (2013), doi:10.1007/s10827-012-0423-7; Epub (October 4, 2012)

    Google Scholar 

  17. Giacco, F., Scarpetta, S.: Attractor networks and memory replay of phase coded spike patterns. In: Frontiers in Artificial Intelligence and Applications, vol. 234, pp. 265–274 (2011)

    Google Scholar 

  18. Scarpetta, S., Giacco, F., Lombardi, F., de Candia, A.: Effects of Poisson noise in a IF model with STDP and spontaneous replay of periodic spatiotemporal patterns, in absence of cue stimulation. Biosystems 112(3), 303–2647 (2013), doi:10.1016/j.biosystems.2013.03.017, ISSN 0303-2647

    Google Scholar 

  19. Scarpetta, S., de Candia, A.: Critical behavior near a phase transition between retrieval and non-retrieval regimes in a LIF network with spatiotemporal patterns. AIP Conf. Proc, vol. 1510, pp. 36–43 (2013), doi:http://dx.doi.org/10.1063/1.4776499

    Google Scholar 

  20. Scarpetta, S., de Candia, A.: Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns. Plos One (accepted May 11) (in press, 2013), doi:10.1371/journal.pone.0064162, PONE-D-13-11021R1

    Google Scholar 

  21. Scarpetta, S., Yoshioka, M., Marinaro, M.: Encoding and Replay of Dynamic Attractors with Multiple Frequencies: Analysis of a STDP Based Learning Rule. In: Marinaro, M., Scarpetta, S., Yamaguchi, Y. (eds.) Dynamic Brain. LNCS, vol. 5286, pp. 38–60. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Gerstner, W., Kempter, R., van Hemmen, L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996)

    Article  Google Scholar 

  23. Abarbanel, H., Huerta, R., Rabinovich, M.I.: Dynamical model of long-term synaptic plasticity. Proc. Nas. Acad. Sci. 99(15), 10132–10137 (2002)

    Article  Google Scholar 

  24. Leibold, C., Kempter, R.: Memory Capacity for Sequences in a Recurrent Network with Biological Constraints. Neural Computation 18(4), 904–941 (2007)

    Article  MathSciNet  Google Scholar 

  25. Kammerer, A.A., Tejero-Cantero, A.A., Leibold Inhibition, C.C.: enhances memory capacity: optimal feedback, transient replay and oscillations. J. Comput. Neurosci. 34(1), 125–136 (2013)

    Article  MathSciNet  Google Scholar 

  26. Lengyel, M., Dayan, P.: Uncertainty, phase, and oscillatory hippocampal recall. Advances in Neural Information Processing Systems 19, 833–840 (2007)

    Google Scholar 

  27. Lengyel, M., Kwag, J., Paulsen, O., Dayan, P.: Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves. Nat. Neurosci. 8, 1677–1683 (2005)

    Article  Google Scholar 

  28. Thurley, K., Leibold, C., Gundlfinger, A., Schmitz, D., Kempter, R.: Phase precession through synaptic facilitation. Neural Computation 20(5), 1285–1324 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  29. Latham, P.E., Lengyel, M.: Phase Coding: Spikes Get a Boost from Local Fields. Curr. Biology 18(8), R349–R351 (2008)

    Google Scholar 

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Correspondence to Silvia Scarpetta .

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Scarpetta, S., De Candia, A. (2014). Effects of Pruning on Phase-Coding and Storage Capacity of a Spiking Network. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-04129-2_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04128-5

  • Online ISBN: 978-3-319-04129-2

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