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

by   Jean Liénard, et al.

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 electrophys-iological 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 suggest 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.


Is Projection Mapping Natural? Towards Physical World Augmentation Consistent with Light Field Context

Projection mapping seamlessly merges real and virtual worlds. Although m...

Chaos-guided Input Structuring for Improved Learning in Recurrent Neural Networks

Anatomical studies demonstrate that brain reformats input information to...

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performa...

Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection

Bias elimination and recent probing studies attempt to remove specific i...

A Model for Combination of External and Internal Stimuli in the Action Selection of an Autonomous Agent

This paper proposes a model for combination of external and internal sti...

Projection predictive variable selection for discrete response families with finite support

The approximate latent-space approach to the projective part of the proj...

Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier

Automatic labeling of coronary arteries is an essential task in the prac...

Please sign up or login with your details

Forgot password? Click here to reset