Elsevier

Neuroscience

Volume 198, 15 December 2011, Pages 171-192
Neuroscience

System
Review
Computational physiology of the neural networks of the primate globus pallidus: function and dysfunction

https://doi.org/10.1016/j.neuroscience.2011.08.068Get rights and content

Abstract

The dorsal pallidal complex is made up of the external and internal segments of the globus pallidus (GPe and GPi respectively). It is part of the main axis of the basal ganglia (BG) that connects the thalamo-cortical networks to the BG input stages (striatum and subthalamic nucleus) and continues directly, and indirectly through the GPe, to the BG output stages (GPi and substantia nigra reticulata). Here we review the unique anatomical and physiological features of the pallidal complex and argue that they support the main computational goal of the BG main axis (actor); namely, a behavioral policy that maximizes future cumulative gains and minimizes costs. The three mono-layer competitive networks of the BG main axis flexibly extract relevant features from the current state of the thalamo-cortical activity to control current (ongoing) and future actions. We hypothesize that the striatal and the subthalamic projections neurons act as mono-stable integrators (class I excitability) and the in-vivo pallidal neurons act as bi-stable resonators (class II excitability). GPe neurons exhibit pausing behavior because their membrane potential lingers in the vicinity of an unstable equilibrium point and bi-stability, and these pauses enable a less-greedy exploratory behavioral policy. Finally, degeneration of midbrain dopaminergic neurons and striatal dopamine depletion (as in Parkinson's disease) lead to augmentation of striatal excitability and competitive dynamics. As a consequence the pallidal network, whose elements tend to synchronize as a result of their bi-stable resonance behavior, shifts from a Poissonian-like non-correlated to synchronous oscillatory discharge mode.

This article is part of a Special Issue entitled: Function and Dysfunction of the Basal Ganglia.

Highlights

▶Three mono-layer competitive networks constitute the main axis (actor) of the basal ganglia. ▶The striatal and the subthalamic projections neurons act as mono-stable integrators (class I excitability). ▶The in-vivo pallidal neurons act as bi-stable resonators (class II excitability). ▶GPe neurons exhibit pausing behavior that supports exploratory behavior because they are closer to the unstable equilibrium point and bi-stability regime. ▶The bi-stable resonance behavior boosts oscillatory synchronization of pallidal neurons following dopamine depletion.

Section snippets

Input/output organization of the basal ganglia and the pallidal network

Perspectives on basal ganglia connectivity have evolved considerably over the years. A comprehensive historical review of this magnificent “relay race” of knowledge is far beyond the scope of this manuscript. Below, we briefly summarize our view of three generations of basal ganglia models.

Cellular anatomy of the pallidal network

The term “globus pallidus” comes from the pale appearance of the globus pallidus in Nissl stains. This is due to the low density of neurons in this structure, which are surrounded by a massive volume of axons (white matter).

Cellular physiology of pallidal cells

Intracellular electrophysiological studies of pallidal cells were initially carried out with sharp microelectrodes on anaesthetized animals (in-vivo). More recent studies have employed slice (in-vitro) preparations in whole-cell and perforated patch configurations. Several studies have been done in tissue culture or in acute dissociated cells. Most of these intracellular studies have been carried out in rodents; nevertheless as in other sections of this review we will use the primate

Spiking activity of neurons in the pallidal network

The core elements of neural computation are the single spikes of single neurons. Today, the best method to study the spiking activity of neurons in a behaving animal is through metal extracellular electrodes (Lemon, 1984). This method, pioneered in the motor cortex by Evarts, 1964, Evarts, 1966) and in the basal ganglia by DeLong, 1971, DeLong, 1972) enables reliable recording of spikes of single or several neurons for tens of minutes and during the performance of behavioral tasks, which

The spiking activity of the pallidal networks following dopamine depletion (Parkinson's disease)

The search for a better understanding of the enigma of the basal ganglia has always been motivated by a drive to find a treatment for the highly prevalent human diseases associated with basal ganglia disorders. The most common of these is Parkinson's disease, which is characterized by motor dysfunctions (akinesia, muscle rigidity, 4–7 Hz rest tremor and postural instability) as well as emotional and cognitive deficits. However, many other human disorders are related to the basal ganglia,

Dynamical system characterization of basal ganglia neurons

The neuronal models used to study network dynamics are usually too simplified to describe the richness of firing patterns exhibited by neurons. On the other hand, full-blown multi-compartmental models are so high-dimensional that they undermine the generality of their predictions. A possible solution to this catch-22 comes from the field of non-linear dynamic analysis (Izhikevich, 2007), which provides theorems that help close this gap.

Computational physiology of the basal ganglia and their disorders

A prominent feature of neural computation is redundancy expansion and reduction of the information processed by different neural networks. The redundancy reduction/expansion process is clearly observed in the relative number of afferent and efferent fibers to and from the CNS in comparison to the number of neurons in the CNS. In the human and non-human primates there are probably several million afferent fibers transferring information from the peripheral receptors to the CNS (e.g. about 1

Discussion

We have focused on the pallidal complex as part of the main axis, or actor part, of the basal ganglia reinforcement learning network. We put forward the hypothesis that this axis achieves its computational goal of efficient feature extraction and redundancy reduction of thalamo-cortical information via its unique anatomical and physiological characteristics. This includes a striking decrease in the number of neurons along the axis of three layers, with mono-stable integrators followed by

Acknowledgments

This study was supported in part by the FP7 select & act and the Vorst family foundation grants (to H.B.). During the preparation of this article JAG was supported by the IDP Foundation, Chicago, Illinois.

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