How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades

Abstract

How does the brain learn to balance between reactive and planned behaviors? The basal ganglia (BG) and frontal cortex together allow animals to learn planned behaviors that acquire rewards when prepotent reactive behaviors are insufficient. This paper proposes a new model, called TELOS, to explain how laminar circuitry of the frontal cortex, exemplified by the frontal eye fields, interacts with the BG, thalamus, superior colliculus, and inferotemporal and parietal cortices to learn and perform reactive and planned eye movements. The model is formulated as fourteen computational hypotheses. These specify how strategy priming and action planning (in cortical layers III, Va and VI) are dissociated from movement execution (in layer Vb), how the BG help to choose among and gate competing plans, and how a visual stimulus may serve either as a movement target or as a discriminative cue to move elsewhere. The direct, indirect and hyperdirect pathways through the BG are shown to enable complex gating functions, including deferred execution of selected plans, and switching among alternative sensory-motor mappings. Notably, the model can learn and gate the use of a What-to-Where transformation that enables spatially invariant object representations to selectively excite spatially coded movement plans. Model simulations show how dopaminergic reward and non-reward signals guide monkeys to learn and perform saccadic eye movements in the fixation, single saccade, overlap, gap, and delay (memory-guided) saccade tasks. Model cell activation dynamics quantitatively simulate seventeen established types of dynamics exhibited by corresponding real cells during performance of these tasks.

Introduction

This article proposes detailed mechanistic solutions to several key problems in sensory-motor control: How does the brain learn to balance between reactive and planned movements? How do recognition and action representations in the brain work together to launch movements toward valued goal objects? How does the brain learn and recall the myriad movement plans it needs to switch among different tasks, when each plan may be sensitive to different combinations of scenic cues and timing constraints?

The article treats these problems by modeling the saccadic, or ballistic, eye movement system. Solving these problems requires interactions among multiple brain regions, including inferotemporal, parietal, and prefrontal cortex (PFC); basal ganglia (BG); amygdala; cerebellum; and superior colliculus (SC). In amphibians and all land vertebrates, the BG interact with a laminar structure, the optic tectum (OT), or its homolog, the SC, to control orienting actions and, in some species, prey-catching actions (Butler and Hodos, 1996, Marin et al., 1998). The mammalian BG also interact with distinct areas of frontal cortex—also laminar structures—to control orienting, cognitive, and manipulative behaviors (Hikosaka and Wurtz, 1989, Passingham, 1993, Strick et al., 1995). Lesions of the BG uniquely cause devastating disorders of the voluntary movement system, e.g. Parkinsonian akinesia, Huntington's chorea, and ballism (Albin, Young, & Penney, 1989). Such observations suggest a tight link between volitional movement and BG interactions with laminar action control structures, which provide a natural basis for differentiating between plan activation and plan execution. Whereas laminar organization has been neglected in most models of BG function, the present model explains how BG interactions with laminar target structures satisfy staging and learning requirements of voluntary behavior.

The model simulates learning and performance of the saccadic tasks that are summarized in Fig. 1 (cf. Hikosaka, Sakamoto, & Usui, 1989, p. 781). It is also used to simulate performance in two related tasks. Recording during such tasks has produced a wealth of electrophysiological data that, in concert with anatomical studies, serve as hard constraints on model development. As a set, these tasks challenge an animal's ability to plan, withhold, and generate goal-directed movements in a way that satisfies instrumental reward contingencies. Simulations (summarized in Fig. 9, Fig. 10, Fig. 11) show that the model shown in Fig. 2 can learn and perform all the tasks, and can regenerate seventeen qualitatively distinct types of task-related activation dynamics exhibited by cells in the SC, BG, thalamus, and oculomotor areas of frontal and parietal cortex.

A key adaptive challenge is to balance reactive and planned movement (Grossberg and Kuperstein, 1986, Hallett, 1978). Rapid reactive movements are needed to ensure survival in response to unexpected dangers. Planned movements often take longer to elaborate. How does the brain prevent reactive movements from being triggered in situations where a more slowly occurring planned movement would be more adaptive? Movement gates can prevent the reactive movement from being launched until the planned movement can effectively compete with it. Then a winning movement command can open its gate and launch its movement. The proposed model shows how a cooperative set of physiological and circuit properties: prevent a reactive movement command from opening the gate before a planned movement command is ready to open it; allow the reactive and planned commands to compete for dominance; yet also allow a reactive movement command to open the gate when no planned movement command is being formed.

Conditional movements towards valued goal objects cannot be made until the goal objects are recognized and movement directions specified. Formidable memory storage problems would ensue if the brain had to learn separate object recognition codes for every retinotopic position and size of an object. To achieve efficient object recognition, the What cortical processing stream builds object representations that are ‘positionally invariant’, i.e. independent of the retinotopic position or size of the object (Bar et al., 2001, Sigala and Logothetis, 2002, Tanaka et al., 1991). Given that recognition codes are independent of position, how does the brain compute how to move to the position of an object after it is recognized? After eliminating the link between an object's identity and position for purposes of object recognition, the brain needs to re-establish this link for purposes of movement. The Where cortical processing stream elaborates the object positions and directions needed to compute motor commands. The model proposes how interactions across the What and Where processing streams overcome their complementary informational deficiencies to generate movements towards recognized objects.

It is not enough to recognize and move towards an object. An animal needs to know when to move towards or away from an object and when not to do so, depending on reward contingencies. Decision criteria include such stimulus properties as color, size, shape, motion and the state of the body, taken individually or in combination. In addition, when confronted with the same scene, an animal may act with respect to different objects depending on its changing needs, such as food if hungry or water if thirsty. The model explains how the brain learns and remembers many plans that involve different sets of discriminative and scheduling constraints, and how it switches among them as needed.

According to the proposed model (Fig. 2), these functional problems find a mechanistic solution in BG interactions with the SC and frontal cortex. Reward-related dopaminergic signals modulate learning in the BGs striatum and the frontal cortex (Gaspar et al., 1995, Giuffrida et al., 1985). The trained BG system allows or prevents movements, according to their appropriateness (Hikosaka and Wurtz, 1983, Bullock and Grossberg, 1991, Crosson, 1985, Mink, 1996, Mink and Thach, 1993, Redgrave et al., 1999). BG outputs provide GABA-ergic inhibitory gating of their target structures. In the primate saccadic circuit, cells in the substantia nigra pars reticulata (SNr) tonically inhibit the SC but pause briefly to allow the SC to generate a saccade (Hikosaka and Wurtz, 1983, Hikosaka and Wurtz, 1989). Lesions in this system can release a ‘visual grasp reflex’ (Guitton, Buchtel, & Douglas, 1985), i.e. impulsive orienting to any visually salient object. Ancient vertebrate genera, such as frogs, already had a well-developed BG system (Marin et al., 1998). Though lacking a precise equivalent of the primate saccadic circuit, frogs can selectively orient while ignoring distracters, but lesions of the BG projection to the OT (SC homolog) impair a frog's ability to orient selectively (Ewert, Schurg-Pfeiffer, & Schwippert, 1996).

Thus BG gates help create a difference between physical and motivational salience. Such gating enables an actor to acquire reward for foveating a physically weak stimulus (e.g. a dim and motionless predator) while ignoring a physically strong stimulus. In most visual scenes, many targets compete for foveation. If the saccadic gate is opened before competition among stimuli resolves, the system may foveate the most contrastive target, or may attempt to foveate multiple targets simultaneously by averaging ambiguous SC activity (Lee et al., 1988, Ottes et al., 1984). In the proposed model, feedforward striatal inhibitory interneurons (Gernert et al., 2000, Koos and Tepper, 1999, Wilson et al., 1989) keep the BG gate shut until competitive dynamics in posterior parietal cortex (PPC) and the frontal eye field (FEF) have a chance to select a unique saccade goal.

The FEF and SC are individually sufficient to generate saccades (Deng et al., 1986, Schiller et al., 1980). Yet in the normal animal, the SC is an important common pathway for saccade generation, and focal SC lesions result in transient impairment of all saccade types (Schiller et al., 1980). Imaging studies (Sweeney et al., 1996) have shown that the frontal cortex is more strongly activated in more difficult oculomotor tasks, e.g. those requiring memory-guided saccades or anti-saccades. Such tasks engage elements of the frontal oculomotor system, including the PFC, FEF, and supplementary eye fields (SEF, an oculomotor area in dorsomedial frontal cortex, DMFC). Lesions of these frontal areas suggest that they incorporate distinct, modular contributions to oculomotor planning and control. The frontal oculomotor areas add the ability to use: head-centered or other non-motor-error coordinates (Schiller, 1998, Schlag and Schlag-Rey, 1987, Schlag-Rey et al., 1997); working memory (Goldman-Rakic, 1987, Goldman-Rakic, 1995, Pierrot-Deseilligny et al., 1993); conjunctions of features (Bichot & Schall, 1999); and internal sequencing (Sommer & Tehovnik, 1999). Taken together, these data suggest a hierarchy. Visual inputs to the SC dominate reactive movements by default, but plans within the frontal cortex can assume control of the SC when simple reactive eye movements are insufficient (Fig. 3).

These principles, realized here as mechanisms in a saccadic control model, should also apply to adaptive control of manipulative and cognitive behaviors. The model and results were briefly reported in Brown, Bullock, & Grossberg (2000).