A Neural Circuit Model for Prospective Control of Interceptive Reaching

Two prospective controllers of hand movements in catching -- both based on required velocity control -- were simulated. Under certain conditions, this required velocity controlled to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint modeL Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.Vrije Universiteit (Gerrit-Jan van Jngen-Schenau stipend of the Faculty of Human Movement Sciences); Royal Netherlands Academy of Arts and Sciences; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Movement Kinematics Dynamically Modulates the Rolandic ~ 20-Hz Rhythm During Goal-Directed Executed and Observed Hand Actions

First Online: 14 February 2018This study investigates whether movement kinematics modulates similarly the rolandic α and β rhythm amplitude during executed and observed goal-directed hand movements. It also assesses if this modulation relates to the corticokinematic coherence (CKC), which is the coupling observed between cortical activity and movement kinematics during such motor actions. Magnetoencephalography (MEG) signals were recorded from 11 right-handed healthy subjects while they performed or observed an actor performing the same repetitive hand pinching action. Subjects’ and actor’s forefinger movements were monitored with an accelerometer. Coherence was computed between acceleration signals and the amplitude of α (8–12 Hz) or β (15–25 Hz) oscillations. The coherence was also evaluated between source-projected MEG signals and their β amplitude. Coherence was mainly observed between acceleration and the amplitude of β oscillations at movement frequency within bilateral primary sensorimotor (SM1) cortex with no difference between executed and observed movements. Cross-correlation between the amplitude of β oscillations at the SM1 cortex and movement acceleration was maximal when acceleration was delayed by ~ 100 ms, both during movement execution and observation. Coherence between source-projected MEG signals and their β amplitude during movement observation and execution was not significantly different from that during rest. This study shows that observing others’ actions engages in the viewer’s brain similar dynamic modulations of SM1 cortex β rhythm as during action execution. Results support the view that different neural mechanisms might account for this modulation and CKC. These two kinematic-related phenomena might help humans to understand how observed motor actions are actually performed.Xavier De Tiège is Postdoctorate Clinical Master Specialist at the Fonds de la Recherche Scientifique (FRS-FNRS, Brussels, Belgium). This work was supported by the program Attract of Innoviris (Grant 2015-BB2B-10 to Mathieu Bourguignon), the Spanish Ministry of Economy and Competitiveness (Grant PSI2016-77175-P to Mathieu Bourguignon), the Marie Skłodowska-Curie Action of the European Commission (grant #743562 to Mathieu Bourguignon), a “Brains Back to Brussels” grant to Veikko Jousmäki from the Institut d’Encouragement de la Recherche Scientifique et de l’Innovation de Bruxelles (Brussels, Belgium), European Research Council (Advanced Grant #232946 to Riitta Hari), the Fonds de la Recherche Scientifique (FRS-FNRS, Belgium, Research Credits: J009713), and the Academy of Finland (grants #131483 and #263800). The MEG project at the ULB-Hôpital Erasme (Brussels, Belgium) is financially supported by the Fonds Erasme

Encoding and control of motor prediction and feedback in the cerebellar cortex

University of Minnesota Ph.D. dissertation. August 2017. Major: Neuroscience. Advisor: Timothy Ebner. 1 computer file (PDF); xi, 162 pages.Extensive research implicates the cerebellum as a forward internal model that predicts the sensory consequences of motor commands and compares them to their actual feedback, generating prediction errors that guide motor learning. However, lacking is a characterization of how information relevant to motor control and sensory prediction error is processed by cerebellar neurons. Of major interest is the contribution of Purkinje cells, the primary output neurons of the cerebellar cortex, and their two activity modalities: simple and complex spike discharges. The dominant hypothesis is that complex spikes serve as the sole error signal in the cerebellar cortex. However, no current hypotheses fully explain or are completely consistent with the spectrum of previous experimental observations. To address these major issues, Purkinje cell activity was recorded during a pseudo-random manual tracking task requiring the continuous monitoring and correction for errors. The first hypothesis tested by this thesis was whether climbing fiber discharge controls the information present in the simple spike firing. During tracking, complex spikes trigger robust and rapid changes in the simple spike modulation with limb kinematics and performance errors. Moreover, control of performance error information by climbing fiber discharge is followed by improved tracking performance, suggesting that it is highly important for optimizing behavior. A second hypothesis tested was whether climbing fiber discharge is evoked by errors in movement. Instead, complex spikes are modulated predictively with behavior. Additionally, complex spikes are not evoked as a result of a specific ‘event’ as has been previously suggested. Together, this suggests a novel function of complex spikes, in which climbing fibers continuously optimize the information in the simple spike firing in advance of changes in behavior. A third hypothesis tested is whether the simple spike discharge is responsible for encoding the sensory prediction errors crucial for online motor control. To address this, two novel manipulations of visual feedback during pseudo-random tracking were implemented to assess whether disrupting sensory information pertinent to motor error prediction and feedback modulates simple spike activity. During these manipulations, the simple spike modulation with behavior is consistent with the predictive and feedback components of sensory prediction error. Together, this thesis addresses a major outstanding question in the field of cerebellar physiology and develops a novel hypothesis about the interaction between the two activity modalities of Purkinje cells

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

Translating novel findings of perceptual-motor codes into the neuro-rehabilitation of movement disorders

The bidirectional flow of perceptual and motor information has recently proven useful as rehabilitative tool for re-building motor memories. We analyzed how the visual-motor approach has been successfully applied in neurorehabilitation, leading to surprisingly rapid and effective improvements in action execution. We proposed that the contribution of multiple sensory channels during treatment enables individuals to predict and optimize motor behavior, having a greater effect than visual input alone. We explored how the state-of-the-art neuroscience techniques show direct evidence that employment of visual-motor approach leads to increased motor cortex excitability and synaptic and cortical map plasticity. This super-additive response to multimodal stimulation may maximize neural plasticity, potentiating the effect of conventional treatment, and will be a valuable approach when it comes to advances in innovative methodologies