Synaptogenesis: Constraining Synaptic Plasticity Based on a Distance Rule

  • Jordi-Ysard Puigbò
  • Joeri van Wijngaarden
  • Sock Ching Low
  • Paul F. M. J. Verschure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)


Neural models, artificial or biologically grounded, have been used for understanding the nature of learning mechanisms as well as for applied tasks. The study of such learning systems has been typically centered on the identification or extraction of the most relevant features that will help to solve a task. Recently, convolutional networks, deep architectures and huge reservoirs have shown impressive results in tasks ranging from speech recognition to visual classification or emotion perception. With the accumulated momentum of such large-scale architectures, the importance of imposing sparsity on the networks to differentiate contexts has been rising. We present a biologically grounded system that imposes physical and local constraints to these architectures in the form of synaptogenesis, or synapse generation. This method guarantees sparsity and promotes the acquisition of experience-relevant, topologically-organized and more diverse features.


Machine learning Connections Biologically constrained 


  1. 1.
    Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing: theory, applications and implementations. In: 15th European Symposium on Artificial Neural Networks, pp. 471–482 (2007)Google Scholar
  2. 2.
    Markov, N., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M., Giroud, P., Ercsey-Ravasz, M., Pilaz, L., et al.: Weight consistency specifies regularities of macaque cortical networks. Cereb. Cortex 21(6), 1254–1272 (2011)CrossRefGoogle Scholar
  3. 3.
    Ercsey-Ravasz, M., Markov, N.T., Lamy, C., Van Essen, D.C., Knoblauch, K., Toroczkai, Z., Kennedy, H.: A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80(1), 184–197 (2013)CrossRefGoogle Scholar
  4. 4.
    Luskin, M.B.: Restricted proliferation and migration of postnatally generated neurons derived from the forebrain subventricular zone. Neuron 11(1), 173–189 (1993)CrossRefGoogle Scholar
  5. 5.
    Kuhn, H.G., Dickinson-Anson, H., Gage, F.H.: Neurogenesis in the dentate gyrus of the adult rat: age-related decrease of neuronal progenitor proliferation. J. Neurosci. 16(6), 2027–2033 (1996)Google Scholar
  6. 6.
    Kelsch, W., Sim, S., Lois, C.: Watching synaptogenesis in the adult brain. Ann. Rev. Neurosci. 33, 131–149 (2010)CrossRefGoogle Scholar
  7. 7.
    Wang, K., Shamma, S.A., Byrne, W.J.: Noise robustness in the auditory representation of speech signals. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1993, vol. 2, pp. 335–338. IEEE (1993)Google Scholar
  8. 8.
    Izhikevich, E.M., et al.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRefGoogle Scholar
  10. 10.
    Abbott, L., Gerstner, W.: Homeostasis and learning through spike-timing dependent plasticity. In: Summer School in Neurophzsics, no. LCN-PRESENTATION-2007-001 (2003)Google Scholar
  11. 11.
    Wang, K., Shamma, S.: Self-normalization and noise-robustness in early auditory representations. IEEE Trans. Speech Audio Process. 2(3), 421–435 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jordi-Ysard Puigbò
    • 1
  • Joeri van Wijngaarden
    • 1
  • Sock Ching Low
    • 1
  • Paul F. M. J. Verschure
    • 1
    • 2
  1. 1.Laboratory of Synthetic, Perceptive, Emotive and Cognitive Science (SPECS), DTICUniversitat Pompeu Fabra (UPF)BarcelonaSpain
  2. 2.Catalan Research Institute and Advanced Studies (ICREA)BarcelonaSpain

Personalised recommendations