Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy

Abstract

Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other.

Keywords: dopamine; gating; reinforcement learning; response selection; response timing; stimulus-response association; working memory.

Figure 1

Sketch of cortico-BG-thalamic fiber tracts and their subdivision into direct, indirect and hyperdirect BG pathways (cf. Bolam et al., 2000). Of the “indirect pathway,” two routes have been proposed (Smith et al., 1998), the short one of which passes from GPe directly to GPi, while the longer one additionally passes through STN.

Figure 2

Empirical findings that constrain interpretations on pathway functions. For illustrative purposes, all findings are shown as cartoons. (A) Striatum receives inputs from both intratelencephalically projecting cortical neurons and cortical pyramidal-tract neurons, while STN predominantly receives pyramidal-tract afferents (Donoghue and Kitai, ; Giuffrida et al., ; Lei et al., ; Parent and Parent, ; Kita and Kita, 2012). (B) Upon electrical stimulation of cortex, direct, indirect and hyperdirect pathways influence GPi activity with different latencies because of their different conduction velocities (Nambu et al., ; Kita and Kita, 2012). (C) Striatal cells innervate relatively small, circumscribed areas of GPi, SNr and GPe dendrites, whereas STN cells innervate these nuclei relatively broadly (Hazrati and Parent, 1992a,b). (D) Dopamine agonists and antagonists oppositely modulate long-term plasticity in cortico-striatal synapses of direct and indirect pathways (cf. Shen et al., 2008). (E) BG are organized in open and closed loops with cortex and thalamus (cf. Alexander et al., ; Joel and Weiner, ; Haber, 2003).

Figure 3

Some influential concepts of BG functions. (A) BG may establish and maintain associations between stimuli and responses (or even between stimuli, responses and outcomes; Redgrave and Gurney, 2006) to allow context-based response selection. (B) BG may contribute to motor timing by providing initiation and termination signals for movements (Nambu, 2004) and by inhibiting premature responding (Frank, 2006). (C) BG may contribute to working memory functions, including gating of information into working memory (Gruber et al., ; O'Reilly and Frank, 2006), working-memory maintenance (Schroll et al., 2012) and production of information from working memory (Schroll et al., 2012). (D) BG may contribute to reinforcement learning processes, including, but not limited to reward-based learning, such that those processes or actions that result in reinforcements will be repeated (e.g., Houk et al., ; Berns and Sejnowski, ; Suri et al., 2001).

Figure 4

Hypotheses on interactions between pathway outputs. Three-dimensional Gaussians depict neuronal activities (z-axis), as elicited by basal-ganglia pathways, for “central” and “surrounding” cortical representations (x- and y-axes). Direct-pathway effects are denoted by red arrows, while the effects of hyperdirect and indirect pathways are denoted by green and blue arrows, respectively. Pointed arrows denote excitatory, rounded arrows inhibitory effects. (A) Push-and-pull opposition: direct and short indirect pathways may oppose each other in a push-and-pull manner, where the effects of direct and short indirect pathways are equal in spatial extent (e.g., Brown et al., ; Frank, ; Schroll et al., 2013). In the example shown here, the direct pathway (thick red arrow) overpowers the short indirect pathway (thin blue arrow). (B) Center-surround cooperation. The direct pathway activates specific cortical representations, while either the hyperdirect pathway (Nambu, 2004) or the long indirect pathway (Mink, 1996) globally inhibit these representations. Since the direct pathway's effect is assumed to be more powerful, center-surround activation emerges. (C) Strict center-surround cooperation. The direct pathway activates specific cortical representations, while the hyperdirect pathway inhibits surrounding (i.e., “competitive”) representations, but not the activated representation itself (Schroll et al., 2013). The resulting effect is mostly equivalent to (B). (D) Center-surround cooperation with global activation. The direct pathway excites cortex relatively globally, while the short indirect pathway inhibits all but the “center” representation (Stocco et al., 2010). As a result, again, the central cortical representation is activated, while its “surrounds” are inhibited. (E) Global blocking of activation. The direct pathway tries to activate specific cortical representations, while the hyperdirect pathway globally inhibits them. In contrast to (B), the hyperdirect pathway is more powerful than the direct pathway and thus overrules any direct-pathway effect (cf. Aron and Poldrack, ; Frank, 2006). Please note that pathway effects are depicted as Gaussians for merely illustrative purposes. Most models do not implement Gaussian functions, but rather assume “box-car” (i.e., all-or-nothing) effects.