Journal of Computational Neuroscience

, Volume 36, Issue 3, pp 445–468 | Cite as

A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection

Article

Abstract

The basal ganglia nuclei form a complex network of nuclei often assumed to perform selection, yet their individual roles and how they influence each other is still largely unclear. In particular, the ties between the external and internal parts of the globus pallidus are paradoxical, as anatomical data suggest a potent inhibitory projection between them while electrophysiological recordings indicate that they have similar activities. Here we introduce a theoretical study that reconciles both views on the intra-pallidal projection, by providing a plausible characterization of the relationship between the external and internal globus pallidus. Specifically, we developed a mean-field model of the whole basal ganglia, whose parameterization is optimized to respect best a collection of numerous anatomical and electrophysiological data. We first obtained models respecting all our constraints, hence anatomical and electrophysiological data on the intrapallidal projection are globally consistent. This model furthermore predicts that both aforementioned views about the intra-pallidal projection may be reconciled when this projection is weakly inhibitory, thus making it possible to support similar neural activity in both nuclei and for the entire basal ganglia to select between actions. Second, we predicts that afferent projections are substantially unbalanced towards the external segment, as it receives the strongest excitation from STN and the weakest inhibition from the striatum. Finally, our study strongly suggests that the intrapallidal connection pattern is not focused but diffuse, as this latter pattern is more efficient for the overall selection performed in the basal ganglia.

Keywords

Basal ganglia Globus pallidus Multiobjective evolutionary algorithms Mean-field models Single-axon tracing studies Antagonist deactivations 

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© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Institut des Systèmes Intelligents et de RobotiqueUniversité Pierre & Marie CurieParisFrance
  2. 2.UMR7222CNRSParisFrance

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