To assess the roles of the cerebellum in motion control on the basis of clinical, anatomical, functional imaging and electrophysiological investigations.
(a) Review of experimental findings starting ...from works of pioneers of the 19th century until 2017, (b) assessment of the theory of internal models.
Anatomically, the cerebellum has a modular organization. Parasagittal bands of Purkinje cells (PCs) project to specific areas of cerebellar nuclei (CN). Functionally, cerebellar cortex (CC) is composed of microzones gathering groups of about 1000 PCs having the same somatotopic receptive field. The intrinsic connectivity networks (ICNs) derived from fMRI studies overlap with maps of structural connectivity. Transsynaptic tracer studies reveal disynaptic pathways linking the cerebellum and basal ganglia. Neurophysiologically, a very robust property of CN is their ability to fire rebound spike bursts following strong hyperpolarization, turning inhibition in CN output spiking. The timing of spiking is critical for the CC and is a pre-requisite for internal models. Anticipation of action is mandatory to plan the sequential movements on the basis of internal/external constraints. Cerebellar dysmetria can now be explained by biased internal models of limb dynamics. Similarly, in Schmahmann’s syndrome a mismatch between reality and perceived reality is suspected.
The leading theory of forward models is now embracing not only the motor symptoms but also the cognitive deficits observed in cerebellar ataxias.
The redundant architecture of the CC makes of the cerebellum an ideal structure to convey signals across microzones in a time-dependent fashion.
Beta oscillations are a dominant feature of the sensorimotor system. A transient and prominent increase in beta oscillations is consistently observed across the sensorimotor cortical-basal ganglia ...network after cessation of voluntary movement: the post-movement beta synchronization (PMBS). Current theories about the function of the PMBS have been focused on either the closure of motor response or the processing of sensory afferance. Computational models of sensorimotor control have emphasized the importance of the integration between feedforward estimation and sensory feedback, and therefore the putative motor and sensory functions of beta oscillations may reciprocally interact with each other and in fact be indissociable. Here we show that the amplitude of sensorimotor PMBS is modulated by the history of visual feedback of task-relevant errors, and negatively correlated with the trial-to-trial exploratory adjustment in a sensorimotor adaptation task in young healthy human subjects. The PMBS also negatively correlated with the uncertainty associated with the feedforward estimation, which was recursively updated in light of new sensory feedback, as identified by a Bayesian learning model. These results reconcile the two opposing motor and sensory views of the function of PMBS, and suggest a unifying theory in which PMBS indexes the confidence in internal feedforward estimation in Bayesian sensorimotor integration. Its amplitude simultaneously reflects cortical sensory processing and signals the need for maintenance or adaptation of the motor output, and if necessary, exploration to identify an altered sensorimotor transformation.
For optimal sensorimotor control, sensory feedback and feedforward estimation of a movement's sensory consequences should be weighted by the inverse of their corresponding uncertainties, which require recursive updating in a dynamic environment. We show that post-movement beta activity (13-30 Hz) over sensorimotor cortex in young healthy subjects indexes the evaluation of uncertainty in feedforward estimation. Our work contributes to the understanding of the function of beta oscillations in sensorimotor control, and provides further insight into how aberrant beta activity can contribute to the pathophysiology of movement disorders.
According to predictive processing theories, vision is facilitated by predictions derived from our internal models of what the world should look like. However, the contents of these models and how ...they vary across people remains unclear. Here, we use drawing as a behavioral readout of the contents of the internal models in individual participants. Participants were first asked to draw typical versions of scene categories, as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent scene categorization experiments. Across two experiments, participants' scene categorization was more accurate for renders tailored to their own drawings compared to renders based on others' drawings or copies of scene photographs, suggesting that scene perception is determined by a match with idiosyncratic internal models. Using a deep neural network to computationally evaluate similarities between scene renders, we further demonstrate that graded similarity to the render based on participants' own typical drawings (and thus to their internal model) predicts categorization performance across a range of candidate scenes. Together, our results showcase the potential of a new method for understanding individual differences – starting from participants' personal expectations about the structure of real-world scenes.
This technical note studies the output consensus problem for a class of heterogeneous uncertain linear multi-agent systems. All the agents can be of any order (which might widely differ among the ...agents) and possess parametric uncertainties that range over an arbitrarily large compact set. The controller uses only the output information of the plant; moreover, the delivered information throughout the communication network is also restricted to the output of each agent. Based on the output regulation theory, it is shown that the output consensus is reached if the (state) consensus is achieved within the internal models among the agent's controllers (even though the plant's outputs, rather than the internal model's outputs, are communicated). The internal models can be designed and embedded into the controller, which provides considerable flexibility to designers in terms of the type of signals that are agreed on among the agents.
Many joint actions require task partners to temporally coordinate actions that follow different spatial patterns. This creates the need to find trade-offs between temporal coordination and spatial ...alignment. To study coordination under incongruent spatial and temporal demands, we devised a novel coordination task that required task partners to synchronize their actions while tracing different shapes that implied conflicting velocity profiles. In three experiments, we investigated whether coordination under incongruent demands is best achieved through mutually coupled predictions or through a clear role distribution with only one task partner adjusting to the other. Participants solved the task of trading off spatial and temporal coordination demands equally well when mutually perceiving each other’s actions without any role distribution, and when acting in a leader-follower configuration where the leader was unable to see the follower’s actions. Coordination was significantly worse when task partners who had been assigned roles could see each other’s actions. These findings make three contributions to our understanding of coordination mechanisms in joint action. First, they show that mutual prediction facilitates coordination under incongruent demands, demonstrating the importance of coupled predictive models in a wide range of coordination contexts. Second, they show that mutual alignment of velocity profiles in the absence of a leader-follower dynamic is more wide-spread than previously thought. Finally, they show that role distribution can result in equally effective coordination as mutual prediction without role assignment, provided that the role distribution is not arbitrarily imposed but determined by (lack of) perceptual access to a partner’s actions.
•Unconscious emotion (UE) remains largely unexplored neuroscientifically.•UE may involve reactions to a situation that are not selected for conscious access.•UE might plausibly be maintained via ...top-down (TMs) or bottom-up (BMs) mechanisms.•TMs may involve thought substitution, suppression, or biased attention.•BMs may involve arousal-induced inhibition of concept-level emotion representations.
While psychiatry and clinical psychology have long discussed the topic of unconscious emotion, and its potentially explanatory role in psychopathology, this topic has only recently begun to receive attention within cognitive neuroscience. In contrast, neuroscientific research on conscious vs. unconscious processes within perception, memory, decision-making, and cognitive control has seen considerable advances in the last two decades. In this article, we extrapolate from this work, as well as from recent neural models of emotion processing, to outline multiple plausible neuro-cognitive mechanisms that may be able to explain why various aspects of one’s own emotional reactions can remain unconscious in specific circumstances. While some of these mechanisms involve top-down or motivated factors, others instead arise due to bottom-up processing deficits. Finally, we discuss potential implications that these different mechanisms may have for therapeutic intervention, as well as how they might be tested in future research.
A Network Perspective on Sensorimotor Learning Sohn, Hansem; Meirhaeghe, Nicolas; Rajalingham, Rishi ...
Trends in neurosciences (Regular ed.),
03/2021, Letnik:
44, Številka:
3
Journal Article
Recenzirano
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What happens in the brain when we learn? Ever since the foundational work of Cajal, the field has made numerous discoveries as to how experience could change the structure and function of individual ...synapses. However, more recent advances have highlighted the need for understanding learning in terms of complex interactions between populations of neurons and synapses. How should one think about learning at such a macroscopic level? Here, we develop a conceptual framework to bridge the gap between the different scales at which learning operates, from synapses to neurons to behavior. Using this framework, we explore the principles that guide sensorimotor learning across these scales, and set the stage for future experimental and theoretical work in the field.
Experimental work on the neural basis of learning has largely focused on single neurons and synapses, yet behavior depends on coordinated interactions between large populations of neurons and synapses.A state space framework has been developed to study dynamics of multidimensional systems, but has not yet been widely adopted to study signatures of learning in neural activity and synaptic weights at a population level.Recent studies have successfully used the state space approach to link behavior to the geometry and structure of neural dynamics.We propose a broader application of the state space framework for understanding learning in terms of coordinated changes across populations of synapses and neurons.The state space framework provides an account of the various timescales of learning, and enables an understanding of the computational principles of learning at a macroscopic level.
The Cerebellum as an Embodying Machine Petrosini, Laura; Picerni, Eleonora; Termine, Andrea ...
The Neuroscientist,
04/2024, Letnik:
30, Številka:
2
Book Review, Journal Article
Recenzirano
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Whereas emotion theorists often keep their distance from the embodied approach, theorists of embodiment tend to treat emotion as a mainly physiologic process. However, intimate links between emotions ...and the body suggest that emotions are privileged phenomena to attempt to reintegrate mind and body and that the body helps the mind in shaping emotional responses. To date, research has favored the cerebrum over other parts of the brain as a substrate of embodied emotions. However, given the widely demonstrated contribution of the cerebellum to emotional processing, research in affective neuroscience should consider embodiment theory as a useful approach for evaluating the cerebellar role in emotion and affect. The aim of this review is to insert the cerebellum among the structures needed to embody emotions, providing illustrative examples of cerebellar involvement in embodied emotions (as occurring in empathic abilities) and in impaired identification and expression of embodied emotions (as occurring in alexithymia).
•Cerebellar generated internal models are disrupted in autism spectrum disorders.•Cerebellar-mediated prediction and adaptation is impaired in autism.•Cerebellar contribution to E/I imbalance in ...autism.•Cerebellar plasticity is disrupted in autism.•Cerebellar-guided implicit learning is impaired in autism.
Autism spectrum disorders (ASD) are highly prevalent neurodevelopmental disorders; however, the neurobiological mechanisms underlying disordered behavior in ASD remain poorly understood. Notably, individuals with ASD have demonstrated difficulties generating implicitly derived behavioral predictions and adaptations. Although many brain regions are involved in these processes, the cerebellum contributes an outsized role to these behavioral functions. Consistent with this prominent role, cerebellar dysfunction has been increasingly implicated in ASD. In this review, we will utilize the foundational, theoretical contributions of the late neuroscientist Masao Ito to establish an internal model framework for the cerebellar contribution to ASD-relevant behavioral predictions and adaptations. Additionally, we will also explore and then apply his key experimental contributions towards an improved, mechanistic understanding of the contribution of cerebellar dysfunction to ASD.
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