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1-5 Systems Control Mechanisms in the CNS

Local networks are interconnected globally throughout the CNS to form neural "systems." A major type of such a system has the general form of a "control system," which consists of a "controller (g)" acting on a "controlled object (G)" (Figure 7A). The controller receives input instruction that provides information about the nature of the required output (e.g., the goal, the trajectory of a movement). In turn, the controller generates command signals that drive the controlled object to respond appropriately. The controller may receive information about the performance of the controlled object (Figure 7A, feedback control), or it may operate without peripheral information (Figure 7B, feedforward control). The goal of a control system is to generate output responses identical to the input instruction. This can be achieved in a feedback control system if g is sufficiently larger than G, but in a feedforward control system, g needs to equal 1/G (Figure 7B). As emphasized by Baev (1999), this basic control system concept applies to various levels of organization within the CNS: in this monograph from reflexes to isolated voluntary movements and finally to coordinated motor actions. In addition, the concept is applied formalistically to cognitive functions.

Figure 7

Figure 7 The fundamental structures of a control system.

(A) A basic feedback control system. (B) A basic feedforward control system. (C) An adaptive control system equipped with an adaptive mechanism. This schematic applies to the cerebellar control of reflexes.

In recent years, modern control theory studies have opened the new fields of "adaptive control" and "model-based control." In adaptive control, the controller is equipped with an adaptive mechanism to constitute an adaptive controller, which learns how to perform effectively in a given situation by altering its performance to match ever-changing environments. When a mechanism is attached to a feedforward controller, their overall input-output relationship f should be adjusted to 1/G (Figure 7C). On the other hand, model-based control was developed for robotic arm control (An et al., 1988), and it has opened a new field of computational neuroscience for research on the cerebellum (Kawato et al., 1987).

In the model-based control, a feedforward control system (Figure 7B) is attached with one of the two types of internal models, "forward" and "inverse" (Figure 8A, B). An internal forward model simulates the kinematics of a controlled object, whereas an internal inverse model simulates the dynamics or kinetics of them (for a definition, see Chapter 15, "Internal Models for Voluntary Motor Control"). Internal forward models support the controller by predicting the state of the system during actual actions. On the other hand, internal inverse models map the relationship between intended actions (or goals) and the motor command to bring about the action. An internal inverse model uses the desired position of the body as inputs to estimate the necessary motor commands, which would transform the current position into the desired one. An adaptive mechanism is involved to secure close simulation by these models. Such models may be formed in various parts of the CNS including, in particular, the elaborate neuronal networks of the cerebellar and cerebral cortices. Hereafter, models formed in the cerebellum and cerebral cortex will be called "cerebellar internal models" and "cerebral cortical models," respectively.

Figure 8

Figure 8 General forms of model-based control systems.

(A) Internal forward model (G') simulates the input-output relationship of the controlled object (G) and is inserted between the output and input of the controller (g). (B) Internal inverse model (1/G) simulates the output-input relationship of the controlled object (G) and is inserted between the input instruction and output response of the controller (g).

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