Non-linear Neuro-inspired Circuits and Systems: Processing and Learning Issues

  • Luca Patanè
  • Roland Strauss
  • Paolo ArenaEmail author
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


In this chapter the main elements useful for the design and realization of the neural architectures reported in the following chapters will be presented. Considering spiking and non-spiking neurons, the models used for implementing each of them, the synaptic models, the basic learning and plasticity algorithms and the network architectures will be introduced and analysed. The key elements that led to their selection and application in the developed neuro-inspired systems will be discussed briefly.


Synaptic Model Echo State Networks (ESN) Mushroom Bodies Reservoir Computing Liquid State Machine (LSM) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abbott, L., DePasquale, B., Memmesheimer, R.: Building functional networks of spiking model neurons. Nat. Neurosci. 19(3), 350–355 (2016)CrossRefGoogle Scholar
  2. 2.
    Arena, E., Arena, P., Patanè, L.: CPG-based Locomotion Generation in a Drosophila-inspired Legged Robot. In: Biorob 2012, pp. 1341–1346. Roma, Italy (2012)Google Scholar
  3. 3.
    Arena, E., Arena, P., Strauss, R., Patanè, L.: Motor-skill learning in an insect inspired neuro-computational control system. Front. Neurorobotics 11, 12 (2017).
  4. 4.
    Arena, P.: The central pattern generator: a paradigm for artificial locomotion. Soft Comput. 4(4), 251–265 (2000). Cited By :19
  5. 5.
    Arena, P., Caccamo, S., Patanè, L., Strauss, R.: A computational model for motor learning in insects. In: International Joint Conference on Neural Networks (IJCNN), pp. 1349–1356. Dallas, TX (2013)Google Scholar
  6. 6.
    Arena, P., De Fiore, S., Patanè, L., Pollino, M., Ventura, C.: Stdp-based behavior learning on tribot robot. Proceedings of SPIE—The International Society for Optical Engineering, vol. 7365, pp. 1–11 (2009).
  7. 7.
    Arena, P., Fortuna, L., Frasca, M., Patanè, L.: A CNN-based chip for robot locomotion control. IEEE Trans. Circuits Syst. I 52(9), 1862–1871 (2005)Google Scholar
  8. 8.
    Arena, P., Fortuna, L., Frasca, M., Patanè, L.: Learning anticipation via spiking networks: application to navigation control. IEEE Trans. Neural Netw. 20(2), 202–216 (2009)Google Scholar
  9. 9.
    Arena, P., Patanè, L.: Simple sensors provide inputs for cognitive robots. IEEE Instrum. Meas. Mag. 12(3), 13–20 (2009).
  10. 10.
    Arshavsky, Y.I., Beloozerova, I.N., Orlovsky, G.N., Panchin, Y.V., Pavlova, G.A.: Control of locomotion in marine mollusc clione limacina iii. on the origin of locomotory rhythm. Exp. Brain Res. 58(2), 273–284 (1985)Google Scholar
  11. 11.
    Barnstedt, O., David, O., Felsenberg, J., Brain, R., Moszynski, J., Talbot, C., Perrat, P., Waddell, S.: Memory-relevant mushroom body output synapses are cholinergic. Neuron 89(6), 1237–1247 (2017).
  12. 12.
    Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, 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)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Büschges, A., Wolf, H.: Nonspiking local interneurons in insect leg motor control. i. common layout and species-specific response properties of femur-tibia joint control pathways in stick insect and locust. J. Neurophysiol. 73(5), 1843–1860 (1995).
  14. 14.
    Chen, Q., Wang, J., Yang, S., Qin, Y., Deng, B., Wei, X.: A real-time FPGA implementation of a biologically inspired central pattern generator network. Neurocomputing 244, 63–80 (2017).
  15. 15.
    Compte, A., Brunel, N., Goldman-Rakic, P., Wang, X.: Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000)Google Scholar
  16. 16.
    Cruse, H.: MMC—a new numerical approach to the kinematics of complex manipulators. Mech. Mach. Theory 37, 375–394 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Durstewitz, D., Seamans, J., Sejnowski, T.: Neurocomputational models of working memory. Nat. Neurosci. 19(3), 1184–1191 (2000)CrossRefGoogle Scholar
  18. 18.
    Erlhagen, W., Bicho, E.: The dynamic neural field approach to cognitive robotics. J. Neural Eng. 3(3), R36 (2006)CrossRefGoogle Scholar
  19. 19.
    Hoppensteadt, F., Izhikevich, E., Arbib, M.A. (eds.): Brain Theory and Neural Networks, vol. 181–186, 2nd edn. MIT press, Cambridge (2002)Google Scholar
  20. 20.
    Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.C., Dudek, P., Hfliger, P., Renaud, S., Schemmel, J., Cauwenberghs, G., Arthur, J., Hynna, K., Folowosele, F., SAGHI, S., Serrano-Gotarredona, T., Wijekoon, J., Wang, Y., Boahen, K.: Neuromorphic silicon neuron circuits. Front. Neurosci. 5(73), 1–23 (2011).
  21. 21.
    Izhikevich, E., Desai, N., Walcott, E., Hoppensteadt, F.: Bursts as a unit of neural information: selective communication via resonance. TRENDS Neurosci. 26(3), 161–167 (2003)CrossRefGoogle Scholar
  22. 22.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  23. 23.
    Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. GMD-Report German National Research Institute for Computer Science 148 (2001)Google Scholar
  24. 24.
    Johnson, J.S., Spencer, J.P., Luck, S.J., Schoner, G.: A dynamic neural field model of visual working memory and change detection. Psychol. Sci. 20(5), 568–577 (2009).
  25. 25.
    Maass, W., Natschlger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)Google Scholar
  26. 26.
    Morris, C., Lecar, H.: Voltage oscillations in the barnacle giant muscle fiber. Biophys. J. 35, 193–213 (1981)Google Scholar
  27. 27.
    Morrison, A., Diesmann, M., Gerstner, W.: Phenomenological models of synaptic plasticity based on spike timing. Biol. Cybern. 98(6), 459–478 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Seo, K., Slotine, J.: Models for global synchronization in cpg-based locomotion. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 281–286 (2007)Google Scholar
  29. 29.
    Siegler, M.V.: Nonspiking interneurons and motor control in insects. Adv. Insect Physiol. 18, 249–304 (1985).
  30. 30.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent plasticity. Nat. Neurosci. 3, 919–926 (2000)CrossRefGoogle Scholar
  31. 31.
    Tani, J.: Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans. Syst. Man Cybern. Part B 26(3), 421–436 (1996)Google Scholar
  32. 32.
    Thalmeier, D., Uhlmann, M., Kappen, H.J., Memmesheimer, R.M.: Learning universal computations with spikes. PLOS Comput. Biol. 12(6), 1–29 (2016).
  33. 33.
    Tuckwell, H.: Introduction to Theoretical Neurobiology. Cambridge UP (1988)Google Scholar
  34. 34.
    Wang, R., Cohen, G., Stiefel, K., Hamilton, T., Tapson, J., van Schaik, A.: An fpga implementation of a polychronous spiking neural network with delay adaptation. Front. Neurosci. 7(14), 1–14 (2013).

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Elettrica Elettronica e dei SistemiUniversity of CataniaCataniaItaly
  2. 2.Institut für Entwicklungsbiologie und NeurobiologieJohannes Gutenberg Universität MainzMainzGermany

Personalised recommendations