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Realizing Medium Spiny Neurons with a Simple Neuron Model

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

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Abstract

Striatal medium spiny neurons (MSNs) constitute input nuclei of the basal ganglia. Most well-known dichotomous of striatal MSNs stem from dopaminergic modulation of striatal processing. Dopamine modulates excitability in striatal MSNs with a complex underlying mechanism and lack of balance in this delicate system leads to pathologies such as Parkinson’s disease. On the contrary, investigation of such a system requires simple, but yet comprehensive models that are capable of capturing complex behaviour of MSNs. We propose a reduced-computational but biologically plausible model that mimics the cell dynamics of striatal D\(_1\)- and D\(_2\)-type MSNs with different levels of dopamine using data from a recent study. Proposed computational model shows good matches to the MSN responses and captures some essential features of MSNs such as first spike latencies, dopamine modulated state transitions and enhanced response to depolarizing input during dopamine intervention.

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Authors and Affiliations

  1. Biomedical Engineering Department, Boğaziçi University, 34342, Bebek, Beşiktaş, Istanbul, Turkey

    Sami Utku Çelikok

  2. Electronics and Telecommunication Department, İstanbul Technical University, Istanbul, Turkey

    Neslihan Serap Şengör

Authors
  1. Sami Utku Çelikok
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  2. Neslihan Serap Şengör
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Correspondence to Sami Utku Çelikok .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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© 2016 Springer International Publishing Switzerland

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Çelikok, S.U., Şengör, N.S. (2016). Realizing Medium Spiny Neurons with a Simple Neuron Model. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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