Computational Mechanisms of Sensorimotor Control David W. Franklin, Daniel M. Wolpert  Neuron  Volume 72, Issue 3, Pages 425-442 (November 2011) DOI: 10.1016/j.neuron.2011.10.006 Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 1 OFC Predicts Adaptive Feedback Responses (A) Paths of reaching movement in the horizontal plane viewed from above. Although moving toward a target (central black circle), the target location could jump laterally (top or bottom targets) at three different times after movement start. These produce different feedback responses depending on the timing of the target shift. (B) Speed profiles for the movements shown in (A). (C) Lateral deviation (toward the displaced target and perpendicular to main movement direction). Subjects make different corrective movements depending on the time of the target shift. (D) An OFC model predicts similar trajectories to human subjects. (E) The complex time varying feedback gains of the optimal feedback controller that give rise to the predicted movements. kp, accuracy of final position; kv, limiting movement velocity; ka, limiting activation; max, maximum. (F and G) Speed and lateral deviation predicted by the optimal control model. Similar to human subjects, the model predicts that when time allows, minimal corrective movements are made to reach the shifted target (low gains in E). On the other hand, when the target jump occurs late in the movement, both the speed and lateral movement are changed, and some undershoot of the movement trajectory is produced (adapted with permission from Liu and Todorov [2007]). Neuron 2011 72, 425-442DOI: (10.1016/j.neuron.2011.10.006) Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 2 Forward Models in the Sensorimotor System: Evidence from the Saccadic System (A) The neural pathway investigated between the visual brain areas and the motor system responsible for saccades. A major visual to motor pathway (blue arrows) takes information from the ventral and dorsal streams, via lateral intraparietal area (LIP) to the FEF. The FEF conveys information to the SC, which triggers saccades via nuclei within the brain stem. The saccadic system also has a motor to visual pathway (red arrows), which was proposed to carry corollary discharge (CD) of the motor actions from the SC to the FEF via the MD of the thalamus. In a series of experiments, Sommer and Wurtz (2002, 2004b, 2006) inactivated the MD relay neurons by injecting muscimol to test if this pathway provided motor information about impending eye saccades to higher visual areas (FEF) similar to forward models. (B) The double-step saccade task. Although the eye is fixating the starting location (gray circle), two targets (orange circles) are flashed briefly that need to be fixated in turn. No visual feedback is present during either of the two saccades. The location of the second target is only available as a vector from the original staring location. Once the eye has made a saccade to the first target, information from the motor system about the movement that occurred is used to update the location of the second target relative to current eye fixation position. If this corollary discharge information is lost, then the position of the second saccade would not be updated, and the second saccade would miss the target (dotted arrow). (C) When the SC-MD-FEF pathway is intact, saccades to the first target exhibit a spread of endpoints (gray ellipse) due to noise; however, the second saccades are angled appropriately to compensate for the variation in starting postures. (D) When the MD replay neurons have been inactivated, the second saccade is not directed at the appropriate angle to the second target. Moreover, the trial-by-trial variations in the endpoint of the first saccade were not compensated for accurately in the second saccade (Sommer and Wurtz, 2002, 2004b). (E) Shifts of the FEF receptive field occur prior to saccade. When fixating (blue cross), an FEF neuron has a receptive field within a particular location (green circle); however, just before a saccade, the receptive field of the neuron shifts to its future location (purple circle). After the saccade, this new location is the receptive field. (F) With an intact SC-MD-FEF pathway (red), probes in the receptive field location elicit neural firing prior to the saccade to the new location (upper plots). Moreover, probes in the future receptive field also elicit neural firing directly prior to the saccade (lower plots), indicating that the receptive field shifts prior to the saccade. When the MD is inactivated (blue), the neural firing in the receptive field is unaffected, but significant deficits (light-blue region) were found to probes in the future receptive field (F adapted by permission from Macmillan Publishers Ltd: Nature, Sommer and Wurtz [2006]). Neuron 2011 72, 425-442DOI: (10.1016/j.neuron.2011.10.006) Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 3 Error-Based Learning Algorithm Predicts Changes in Muscle Activation and Coactivation (A) We consider a single-joint motion for simplicity. In this example, the elbow joint is controlled by two antagonist muscles, the biceps (red) and triceps (blue). During a movement the current joint angle is compared to the desired joint angle to give rise to a sequence of errors. (B) Each error measure is used by the V-shaped update rule to determine the change in muscle activation for the next repetition of the movement. This change in muscle activation is shifted forward in time on the subsequent trial to compensate for the delays. The V-shaped learning rule for each muscle (biceps, red; triceps, blue) has a different slope depending on whether the error indicates that the muscle is too long or too short at each point in time. (C) The different slopes for stretch or shortening of each muscle lead to an appropriate change in the reciprocal muscle activation that drives compensatory changes in the joint torques and endpoint forces. (D) Large errors lead to an increase in coactivation, whereas little or no error leads to a reduction in the coactivation. (E) Changes in muscle activation patterns during repeated trials. In the steady-state condition a movement may be produced by coordinated feedforward activation of an agonist-antagonist pair (trial 0). The introduction of a disturbance that extends the elbow joint (trial 1) produces a large feedback response in the biceps muscle (dark red). On the subsequent trial (trial 2), the learning algorithm changes both the biceps and triceps activation pattern (shifted forward in time from the feedback response). Due to this action, a smaller feedback response is produced on this trial. In the next trial (trial 3), the feedforward activation is again increased based on the error on the previous trial such that the disturbance is compensated for perfectly. This leads to a reduction in the coactivation on the next trial (trial 4). (B)–(D) were redrawn with permission from Franklin et al. (2008). Neuron 2011 72, 425-442DOI: (10.1016/j.neuron.2011.10.006) Copyright © 2011 Elsevier Inc. Terms and Conditions