Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory–motor adaptation

Luque, Niceto R.; Naveros, Francisco; Abadìa, Ignacio; Ros, Eduardo; Arleo, Angelo

doi:10.1016/j.neunet.2022.08.020

Cited by 5 publications

(16 citation statements)

References 63 publications

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“…To ensure a proper representation of the entire retinal slip region over trials, i.e., desired vs. actual eye velocity, we generated external input activity driven towards the IO cells using a probabilistic spike sampling of the retinal slip signals (instructive signal generation) according to a Poisson process, whilst maintaining the IO activity between 1 and 10 Hz per fibre (similar to electrophysiological data [22]). This approach allowed us to accurately sample the retinal slip evolution even at such a low frequency, as supported by previous studies [25, 27-29, 34, 35]. We assumed that the CF triggered burst signals based on retinal slip magnitude, supported by two findings in awake mice: (i) CF-triggered signals gradually increases with the duration and pressure of periocular stimuli [36, 37] and (ii) The amplitude of CF-triggered signals onto PC dendrites is graded and represents information about the intensity of sensory stimuli [18, 38].…”

Section: Discussionsupporting

confidence: 62%

“…PCs were also driven by the climbing fibres (CFs, i.e., IO axons), which conveyed the teaching signal encoding retinal slip errors. The excitatory olivary CF collaterals along with inhibitory PC outputs contacted MVN neurons, which closed the loop through the MVN-IO inhibitory connections [25] conforming the olivo-cortico-nucleo-olivary (OCNO) loop [26]. MVN generated the cerebellar output that was sent to the oculomotor neurons, which ultimately drove eye movements.…”

Section: Resultsmentioning

confidence: 99%

“…The LIF model used for MFs and GCs was the same as the one used in [27]. However, the LIF model used for MVN was implemented based on [25] following equations 7-13. The neural dynamics of MVN were defined by the membrane potential and the presence of excitatory (AMPA and NMDA) and inhibitory (GABA) chemical synapses.…”

mentioning

confidence: 99%

“…Cerebellar motor adaptation was driven by two spike-timing dependent plasticity (STDP) mechanisms at PF-PC and MF-MVN synapses. During 500 s of simulation, plasticity shaped PF-PC and MF-MVN synaptic efficacies (which were randomly initialised) to adapt VOR and reduce retinal slips[25,[27][28][29]…”

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confidence: 99%

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The role of olivary phase-locking oscillations in cerebellar sensorimotor adaptation

Luque,

Carrillo,

Naveros

et al. 2024

Preprint

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The function of the olivary nucleus is key to cerebellar adaptation as it modulates long term synaptic plasticity between parallel fibres and Purkinje cells. Here, we posit that the neural dynamics of the inferior olive (IO) network, and in particular the phase of subthreshold oscillations with respect to afferent excitatory inputs, plays a role in cerebellar sensorimotor adaptation. To test this hypothesis, we first modelled a network of 200 multi-compartment Hodgkin-Huxley IO cells, electrically coupled via anisotropic gap junctions. The model IO neural dynamics captured the properties of real olivary activity in terms of subthreshold oscillations and spike burst responses to dendritic input currents. Then, we integrated the IO network into a large-scale olivo-cerebellar model to study vestibular ocular reflex (VOR) adaptation. VOR produces eye movements contralateral to head motion to stabilise the image on the retina. Hence, studying cerebellar-dependent VOR adaptation provided insights into the functional interplay between olivary subthreshold oscillations and responses to retinal slips (i.e., image movements triggering optokinetic adaptation). Our results showed that the phase-locking of IO subthreshold oscillations to retina slip signals is a necessary condition for cerebellar VOR learning. We also found that phase-locking makes the transmission of IO spike bursts to Purkinje cells more informative with respect to the variable amplitude of retina slip errors. Finally, our results showed that the joint action of IO phase-locking and cerebellar nuclei GABAergic modulation of IO cells' electrical coupling is crucial to increase the state variability of the IO network, which significantly improves cerebellar adaptation.

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Section: Discussionsupporting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The role of olivary phase-locking oscillations in cerebellar sensorimotor adaptation

Luque,

Carrillo,

Naveros

et al. 2024

Preprint

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“…Despite the well-accepted common ground on the cerebellum established by the Marr-Albus-Ito theory, new findings keep refining the understanding about cerebellar structure and operation, for which computational models are key contributors [15]. Computational models of the cerebellum have been used to study its inner dynamics [16, 17], as well as harnessing cerebellar motor adaptation capabilities to develop adaptive controllers based on internal model building [18, 19].…”

Section: Introductionmentioning

confidence: 99%

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation

Bruel

Abadìa

Collin

et al. 2023

Preprint

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Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same benchmark. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.

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Dopamine-dependent cerebellar dysfunction enhances beta oscillations and disrupts motor learning in a multiarea model

Gambosi,

Sheiban,

Biasizzo

et al. 2023

Preprint

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Parkinson's disease (PD) is a chronic degenerative disorder of the central nervous system that affects the motor system. The discovery that PD motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of PD research on the basal ganglia. However, recent findings point to an active involvement of the cerebellum in PD. Here, we have developed a multiscale computational model of the rodent brain's basal ganglia-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of PD neural activity, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in PD. Overall, these results suggest a relevant involvement of cerebellum in PD motor symptoms.

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Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory–motor adaptation

Cited by 5 publications

References 63 publications

The role of olivary phase-locking oscillations in cerebellar sensorimotor adaptation

The role of olivary phase-locking oscillations in cerebellar sensorimotor adaptation

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation

Dopamine-dependent cerebellar dysfunction enhances beta oscillations and disrupts motor learning in a multiarea model

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