Dataset Information

A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

ABSTRACT: To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To date, little is known about the role of the cerebellum in asymmetrical adaptation. Here, we investigate the cerebellar mechanisms required for asymmetrical adaptation using a bi-hemispheric cerebellar neuronal network model (biCNN). The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically. The biCNN model was constructed to run in real-time and used to control an unstable two-wheeled balancing robot. The load of the robot and its environment were modified to create asymmetrical perturbations. Plasticity at parallel fiber-Purkinje cell synapses in the biCNN model was driven by error signal in the climbing fiber (cf) input. This cf input was configured to increase and decrease its firing rate from its spontaneous firing rate (approximately 1 Hz) with sensory errors in the preferred and non-preferred direction of each hemisphere, as demonstrated in the monkey cerebellum. Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller. Further, the spontaneous activity of the cf, while relatively small, was critical for balancing the contribution of each cerebellar hemisphere to the overall motor command sent to the robot. Eliminating the spontaneous activity compromised the asymmetrical learning capabilities of the biCNN model. Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

SUBMITTER: Pinzon-Morales RD

PROVIDER: S-EPMC4221029 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

Pinzon-Morales Ruben-Dario RD Hirata Yutaka Y

Frontiers in neural circuits 20141105

To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To da ...[more]

PMID: 25414644

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Project description:Functionally mature neural circuits are shaped during postnatal development by eliminating redundant synapses formed during the perinatal period. In the cerebellum of neonatal rodents, each Purkinje cell (PC) receives synaptic inputs from multiple (more than 4) climbing fibers (CFs). During the first 3 postnatal weeks, synaptic inputs from a single CF become markedly larger and those from the other CFs are eliminated in each PC, leading to mono-innervation of each PC by a strong CF in adulthood. While molecules involved in the strengthening and elimination of CF synapses during postnatal development are being elucidated, much less is known about the molecular mechanisms underlying CF synapse formation during the early postnatal period. Here, we show experimental evidence that suggests that a synapse organizer, PTPδ, is required for early postnatal CF synapse formation and the subsequent establishment of CF to PC synaptic wiring. We showed that PTPδ was localized at CF-PC synapses from postnatal day 0 (P0) irrespective of the expression of Aldolase C (Aldoc), a major marker of PC that distinguishes the cerebellar compartments. We found that the extension of a single strong CF along PC dendrites (CF translocation) was impaired in global PTPδ knockout (KO) mice from P12 to P29-31 predominantly in PCs that did not express Aldoc [Aldoc (-) PCs]. We also demonstrated via morphological and electrophysiological analyses that the number of CFs innervating individual PCs in PTPδ KO mice were fewer than in wild-type (WT) mice from P3 to P13 with a significant decrease in the strength of CF synaptic inputs in cerebellar anterior lobules where most PCs are Aldoc (-). Furthermore, CF-specific PTPδ-knockdown (KD) caused a reduction in the number of CFs innervating PCs with decreased CF synaptic inputs at P10-13 in anterior lobules. We found a mild impairment of motor performance in adult PTPδ KO mice. These results indicate that PTPδ acts as a presynaptic organizer for CF-PC formation and is required for normal CF-PC synaptic transmission, CF translocation, and presumably CF synapse maintenance predominantly in Aldoc (-) PCs. Furthermore, this study suggests that the impaired CF-PC synapse formation and development by the lack of PTPδ causes mild impairment of motor performance.

Dataset Information

A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

Publications

A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

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