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Interactions in the Striatal Network with Different Oscillation Frequencies
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.ORCID iD: 0000-0002-8044-9195
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.ORCID iD: 0000-0002-0550-0739
2017 (English)In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science, Springer, 2017, Vol. 10613, 129-136 p.Conference paper, Published paper (Refereed)
Abstract [en]

Simultaneous oscillations in different frequency bands are implicated in the striatum, and understanding their interactions will bring us one step closer to restoring the spectral characteristics of striatal activity that correspond to the healthy state. We constructed a computational model of the striatum in order to investigate how different, simultaneously present, and externally induced oscillations propagate through striatal circuitry and which stimulation parameters have a significant contribution. Our results show that features of these oscillations and their interactions can be influenced via amplitude, input frequencies, and the phase offset between different external inputs. Our findings provide further untangling of the oscillatory activity that can be seen within the striatal network.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10613, 129-136 p.
Keyword [en]
Corticostriatal network, Network oscillations, GABAergic transmission, Basal ganglia, Cortex
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-217110DOI: 10.1007/978-3-319-68600-4_16ISBN: 978-3-319-68599-1 (print)OAI: oai:DiVA.org:kth-217110DiVA: diva2:1153754
Conference
Artificial Neural Networks and Machine Learning – ICANN 2017.
Note

QC 20171101

Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-01-13Bibliographically approved

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Publisher's full texthttps://link.springer.com/chapter/10.1007/978-3-319-68600-4_16

Authority records BETA

Kumar, ArvindHellgren Kotaleski, Jeanette

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