Skip to main content
SpringerLink
Log in

Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

Included in the following conference series:

Abstract

In temporal information processing, both the cerebellum and the basal ganglia play essential roles. In particular, for interval timing learning, the cerebellum exhibits temporally localized activity around the onset of the unconditioned stimulus, whereas the basal ganglia represents the passage of time by their ramping-up activity from the onset of the conditioned stimulus to that of the unconditioned stimulus. We present a unified computational model of the cerebellum and the basal ganglia for the interval timing learning task. We report that our model reproduces the localized activity in the cerebellum and the gradual increase of the activity in the basal ganglia. These results suggest that the cerebellum and the basal ganglia play different roles in temporal information processing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Buhusi, C.V., Meck, W.H.: What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6, 755–765 (2005)

    Article  Google Scholar 

  2. Ivry, R.B., Spencer, R.M.: The neural representation of time. Curr. Opin. Neurobiol. 14, 225–232 (2004)

    Article  Google Scholar 

  3. Mauk, M.D., Garcia, S., Medina, J.F., Steele, P.M.: Does cerebellar LTD mediate motor learning? Toward a resolution without a smoking gun. Neuron 20, 359–362 (1998)

    Article  Google Scholar 

  4. Tanaka, M.: Cognitive signals in the primate motor thalamus predict saccade timing. J. Neurosci. 27, 12109–12118 (2007)

    Article  Google Scholar 

  5. Tanaka, M., Kunimatsu, J.: Contribution of the central thalamus to the generation of volitional saccades. Eur. J. Neurosci. 33, 2046–2057 (2011)

    Article  Google Scholar 

  6. McCormick, D.A., Thompson, R.F.: Neuronal responses of the rabbit cerebellum during acquisition and performance of a classically conditioned nictitating membrane-eyelid response. J. Neurosci. 4, 2811–2822 (1984)

    Google Scholar 

  7. Tanaka, M., Kunimatsu, J., Ohmae, S.: Neural representation of time. Brain Nerve 65, 941–948 (2013)

    Google Scholar 

  8. Kandel, E.R., Schwartz, J.H., Jessell, T.M., Siegelbaum, S.A., Hudspeth, A.J.: Principles of Neural Science, 5th edn. McGraw-Hill Companies Inc., New York (2013)

    Google Scholar 

  9. Kita, H., Tachibana, Y., Nambu, A., Chiken, S.: Balance of monosynaptic excitatory and disynaptic inhibitory responses of the globus pallidus induced after stimulation of the subthalamic nucleus in the monkey. J. Neurosci. 25, 8611–8619 (2005)

    Article  Google Scholar 

  10. Mauk, M.D., Donegan, N.H.: A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum. Learn. Memory 3, 130–158 (1997)

    Article  Google Scholar 

  11. Yamazaki, T., Tanaka, S.: Neural modeling of an internal clock. Neural Comput. 17, 1032–1058 (2005)

    Article  MATH  Google Scholar 

  12. Yamazaki, T., Tanaka, S.: A neural network model for trace conditioning. J. Neural Syst. 15, 23–30 (2005)

    Article  Google Scholar 

  13. Mamad, O., Delaville, C., Benjelloun, W., Benazzouz, A.: Dopaminergic control of the globus pallidus through activation of D2 receptors and its impact on the electrical activity of subthalamic nucleus and substantia nigra reticulata neurons. PLoS ONE 10(3), 1–16 (2015)

    Article  Google Scholar 

Download references

Acknowledgment

We would like to thank Professor Masaki Tanaka at Hokkaido University for fruitful discussions on his relay hypothesis. Part of this work was supported by JSPS KAKENHI Grant Number 26119511. This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

Author information

Authors and Affiliations

  1. Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

    Ohki Katakura & Tadashi Yamazaki

  2. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Tadashi Yamazaki

Authors
  1. Ohki Katakura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tadashi Yamazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ohki Katakura .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Katakura, O., Yamazaki, T. (2016). Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46681-1_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation