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A Neural Model of Cerebellar Learning for Arm Movement Control: Cortico-Spino-Cerebellar Dynamics Jose L

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A Neural Model of Cerebellar Learning for Arm Movement Control: Cortico-Spino-Cerebellar Dynamics Jose L Downloaded from learnmem.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press A Neural Model of Cerebellar Learning for Arm Movement Control: Cortico-Spino-Cerebellar Dynamics Jose L. Contreras-Vidal, 1 Stephen Grossberg, 2 and Daniel Bullock Department of Cognitive and Neural Systems and Center for Adaptive Systems Boston University Boston, Massachusetts 02215 Abstract discharges maintains stable learning. Long-term potentiation (LTP) in response to A neural network model of opponent uncorrelated parallel fiber signals enables cerebellar learning for arm movement previously weakened synapses to recover. control is proposed. The model illustrates Loss of climbing fibers, in the presence of how a central pattern generator in cortex LTP, can erode normal opponent signal and basal ganglia, a neuromuscular force processing. Simulated lesions of the controller in spinal cord, and an adaptive cerebellar network reproduce symptoms of cerebellum cooperate to reduce motor cerebellar disease, including sluggish variability during multijoint arm movements movement onsets, poor execution of using mono- and bi-articular muscles. multijoint plans, and abnormally prolonged Cerebellar learning modifies velocity endpoint oscillations. commands to produce phasic antagonist bursts at interpositus nucleus cells whose feed-forward action overcomes inherent Introduction limitations of spinal feedback control of tracking. Excitation of ct motoneuron pools, Clinical and experimental data on both oculo- combined with inhibition of their Renshaw motor and skeletomotor systems suggest that an cells by the cerebellum, facilitate movement intact cerebellum is neccssary for the learning and initiation and optimal execution. performance of accurate, rapid movements, espe- Transcerebellar pathways are opened by cially movements involving multiple degrees of learning through long-term depression freedom. Systems with multiple, mechanically (LTD) of parallel fiber-Purkinje cell linked degrees of freedom are difficult to control synapses in response to conjunctive because the inertial, gravitational, and interaction stimulation of parallel fibers and climbing forces, which must be compensated whencver fiber discharges that signal muscle stretch they do not assist desired motion or stasis, are con- errors. The cerebellar circuitry also learns to figuration- and rate-dependent. In very slow move- control opponent muscles pairs, allowing ments, there can be sufficient time to use scnsory cocontraction and reciprocal inhibition of feedback to adjust motor commands before errors muscles. Learning is stable, exhibits load caused by uncompensated forces can grow very compensation properties, and generalizes large. But as movement speeds increase, feedback better across movement speeds if begins to arrive too late to be useful for on-line motoneuron pools obey the size principle. control of the current movement. In such cases, The intermittency of climbing fiber accuracy cannot be guaranteed unless motor com- mands to entire groups of musclcs can be timed accurately and scaled prior to the arrival of feed- back. 1present address: Motor Control Laboratory, Arizona State University, Tempe, Arizona 85287-0404. Such considcrations, in combination with ob- 2Corresponding author. servations regarding the cerebellum's typical "side- LEARNING & MEMORY 3:475-502 9 1997by Cold Spring Harbor Laboratory Press ISSN1072-0502/97 $5.00 L E A R N / N G & M E M O R Y 475 Downloaded from learnmem.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Contreras-Vidal et al. loop" embedding (Fig. 1, below; Ito 1984) within gists (degrees of freedom) be activated at different the sensory-motor system, support a view of the times after detection of a context stimulus. In con- cerebellum as an adaptive controller capable of trast, several features of cerebellar architecture and calibrating parallel feed-forward motor commands dynamics qualify it as the best known candidate for that can substitute for slow feedback-based com- such a sensory-motor memory: the cerebellum's mands and thereby enable high speed and accu- side4oop embedding, the large-scale projection of racy without iterations (Grossberg and Kuperstein state-specifying afferents to the granule cell layer of 1986). Relatedly, the cerebellum has been viewed the cerebellar cortex, the further fanout from gran- by some as an adaptive calibrator of internal for- ule cells of the parallel fibers (PFs), and the long- ward models that allow computation of the ex- term plasticity of PF-Purkinje synapses--now dem- pected effects of motor commands (Kawato and onstrated both in vitro and in vivo (e.g., Crepel et Gomi 1991; Miall et al. 1996). This application of a al. 1996). Moreover, recent modeling studies indi- cerebellar side-loop would allow substitution of cate how the specialized biochemistry of Purkinje fast, "internal feedback" for slow, sensor-based cells, particularly the slowly developing calcium feedback. These two views are not incompatible responses of the metabotropic glutamate receptor (Stein and Glickstein 1992). In both perspectives, cascade, can allow the cerebellum to learn timed the cerebellum learns to substitute a faster, antici- sequences of motor control signals (Fiala and Bul- patory process for a slower, reactive one. Also lock 1996; Fiala et al. 1996; Kano 1996). compatible with both approaches is the hypothesis Analyzing the distinct functions of elements of that external feedback, although rendered progres- the cerebellar system requires systematic modeling sively less important for on-line control as cerebel- and simulations. By this we mean simulations in lar learning progresses, retains a critical teaching which the model cerebellar system includes all of role; such feedback is ultimately the only reliable the major cell types; the model cerebellum's affer- source of information regarding the adequacy of ent signals are generated by simulated receptors the timing and scaling of feed-forward movement and circuits that are part of the simulation; and its commands, or of internal model calibration. At the efferent signals affect the simulated motor plant via level of the cerebellar system, these approaches a simulated spinal circuit. In such a model, all ma- thus postulate that feedback error signals in motor jor elements of the cerebellar system can be exam- coordinates are converted by the inferior olive into ined for their contribution, if any, to improved op- discrete teaching signals, which guide incremental, eration of a sensory-motor system whose dynamics trial-by-trial adjustments in the cerebellar adaptive are realistic enough to appropriately challenge cer- weights that control anticipatory actions. ebellar provisions for learning, memory, and per- Although supported by a wealth of data, the formance. This report describes such a model, view of the cerebellum as an adaptive controller with a focus on how the cerebellar-olivary side- capable of using feedback to learn how to substi- loop can exert learned control of opponently or- tute a faster for a slower "solution" in repeatable ganized limb spinal circuits and thereby improve task contexts has not gone unchallenged. Brain the performance of rapid two-joint movements, stem and spinal cord circuitry apparently provide whose desired form is determined by output from some basis for task-specific recruitment and con- a central pattern generator. trol of muscle synergies, and experimental studies Because the cerebellar model interacts with a of decerebrate/decerebellate animals have pro- two-joint limb moved by sets of opponent muscles, duced evidence suggesting that such circuitry has the simulations are pertinent to issues of oppo- some of the same associative competence widely nency both within the cerebellar system and in the attributed to the cerebellum (Bloedel and Bracha descending pathways from the cerebellum to the 1995). Although suggestive of partial redundancy, spinal cord. Regarding the latter, there has been such studies have not shown that the brain stem/ great interest in recent years in the role of the red spinal circuitry affords a large, content-addressable nucleus (RN) of the reticular formation and the memory for learning and storage of the relatively associated cerebello-rubro-spinal pathway. In many arbitrary task contexts and action groupings re- vertebrates, this pathway is a key element in the quired for a large repertoire of learned skills. Nor system for voluntary movement, and its analysis has it been shown that such circuitry is capable of promises to illuminate cerebellar function because learning to generate novel movements whose ac- the magnocellular division of the RN is dominated curate performance requires that different syner- by inputs from the nucleus interpositus (NIP) of L E A R N ! N G & M E M O R Y 476 Downloaded from learnmem.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press CEREBELLAR LEARNING FOR ARM MOVEMENT CONTROL the cerebellum (e.g., Robinson et al. 1987). Much form of the model, these learning results also re- prior modeling work has focused on the role of the veal that muscle opponency imposes no con- NIP/RN in eye blink conditioning (e.g., Bartha et straints on prewiring at the level of the cerebellar al. 1991; Fiala et al. 1996). The comprehensive cortex other than the constraint that CFs carrying skeletomotor simulation strategy followed here re- signals of errors from opponent muscle channels fines and
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