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.
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Zheng, Y., Schwabe, L., Plotkin, J.L. (2014). Location-Dependent Dendritic Computation in a Modeled Striatal Projection Neuron. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_93
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DOI: https://doi.org/10.1007/978-3-319-11179-7_93
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
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