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Location-dependent dendritic computation in a modeled striatal projection neuron

  • Northwestern University
  • University of Rostock

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The striatum comprises part of a feedback loop between the cerebral cortex, thalamus and other nuclei of the basal ganglia, ultimately guiding action selection and motor learning. Much of this is facilitated by striatal projection neurons, which receive and process highly convergent cortical and thalamic excitatory inputs. All of the glutamatergic inputs to projection neurons synapse on dendrites, many directly on spine heads. The distal, but not proximal, dendrites of projection neurons are capable of supporting synaptically driven regenerative events, which are transfered to the soma as depolarized upstates from which action potentials can occur. In this study we present a modified NEURON model of a striatal projection neuron, and use it to examine the location-dependence of upstate generation and action potential gating. Specifically, simulations show that the small diameter of distal SPN dendrites can support plateau potentials by increasing the cooperativity among neighboring spines. Furthermore, such distally evoked plateaus can boost the somatic response to stimulation of proximal dendritic spines, facilitating action potential generation. The implications these results have for action selection are discussed.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Pages741-748
Number of pages8
ISBN (Print)9783319111780
DOIs
StatePublished - 2014
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: Sep 15 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Artificial Neural Networks, ICANN 2014
Country/TerritoryGermany
CityHamburg
Period09/15/1409/19/14

Keywords

  • association-based information processing
  • Computational neuroscience
  • dendrite
  • striatum
  • upstate

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