Advertisement

Sensorimotor Control Learning Using a New Adaptive Spiking Neuro-Fuzzy Machine, Spike-IDS and STDP

  • Mohsen Firouzi
  • Saeed Bagheri Shouraki
  • Jörg Conradt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based MIMO system as logical combination of spatially distributed Single-Input Single-Output sub-systems. Each SISO systems as sensory and processing layer of the inference system, construct a single rule and learning process is handled by a Hebbian-like Spike-Time Dependent Plasticity. To shape a concrete knowledge about the whole system, extracted features of SISO neural systems (or equivalently the rules associated with SISO systems) are combined. To exhibit the system applicability, a single link cart-pole balancer as a sensory-motor learning task, has been simulated. The system is provided by reinforcement feedback from environment and is able to learn how to get expert and achieve a successful policy to perform motor control.

Keywords

Sensorimotor Control Learning Spiking Neural Networks Neuro- Fuzzy Spike Time Dependent Plasticity Cart-Pole balancing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kolman, E., Margaliot, M.: Knowledge-based neurocomputing: A fuzzy logic approach. STUDFUZZ, vol. 234, pp. 1–5. Springer, Heidelberg (2009)Google Scholar
  2. 2.
    Shouraki, S.B., Honda, N., Yuasa, G.: Fuzzy interpretation of human intelligence. International Journal of Fuzziness and knowledge-Based Systems 7(4), 407–414 (1999)CrossRefzbMATHGoogle Scholar
  3. 3.
    Polkinghorne, J.: Belief in god in an age of science, pp. 25–48. Yale University Press, New Haven (1998)Google Scholar
  4. 4.
    Bohte, S.M., La Poutre, H., Kok, J.N.: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer rbf networks. IEEE Transactions on Neural Networks 13(2), 426–435 (2002)CrossRefGoogle Scholar
  5. 5.
    Firouzi, M., Shouraki, S.B., Afrakuti, I.E.P.: Pattern Analysis by Active Learning Method Classifier. Journal of Intelligent & Fuzzy Systems 26(1), 49–62 (2014)Google Scholar
  6. 6.
    Shadmehr, R., Smith, M.A., Krakauer, J.W.: A computional neuroanatomy for motor control. Exp. Brain. Res. 185(3), 359–381 (2008)CrossRefGoogle Scholar
  7. 7.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models, 1st edn. The Cambridge University Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohsen Firouzi
    • 1
    • 2
    • 3
  • Saeed Bagheri Shouraki
    • 4
  • Jörg Conradt
    • 1
    • 2
    • 3
  1. 1.Neuroscientific System TheoryTechnische Universität MünchenGermany
  2. 2.Bernstein Center for Computational NeuroscienceMünchenGermany
  3. 3.Graduate School of Systemic Neurosciences-Ludwig-Maximilians-UniversitätMünchenGermany
  4. 4.Research Group of Brain Simulation and Cognitive Science, ACLSharif University of TechnologyTehranIran

Personalised recommendations