Kurt A. Thoroughman

Learning of action through adaptive combination of motor primitives.

Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a systems ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brains ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects’ adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.

Rapid Reshaping of Human Motor Generalization

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

Activity-dependent modification of inhibitory synapses in models of rhythmic neural networks

The faithful production of rhythms by many neural circuits depends critically on the strengths of inhibitory synaptic connections. We propose a model in which the strengths of inhibitory synapses in a central pattern-generating circuit are subject to activity-dependent plasticity. The strength of each synapse is modified as a function of the global activity of the postsynaptic neuron and by correlated activity of the pre- and postsynaptic neurons. This allows the self-assembly, from random initial synaptic strengths, of two cells into reciprocal oscillation and three cells into a rhythmic triphasic motor pattern. This self-assembly illustrates that complex oscillatory circuits that depend on multiple inhibitory synaptic connections can be tuned via simple activity-dependent rules.

Motor adaptation scaled by the difficulty of a secondary cognitive task.

Background Motor learning requires evaluating performance in previous movements and modifying future movements. The executive system, generally involved in planning and decision-making, could monitor and modify behavior in response to changes in task difficulty or performance. Here we aim to identify the quantitative cognitive contribution to responsive and adaptive control to identify possible overlap between cognitive and motor processes. Methodology/Principal Findings We developed a dual-task experiment that varied the trial-by-trial difficulty of a secondary cognitive task while participants performed a motor adaptation task. Subjects performed a difficulty-graded semantic categorization task while making reaching movements that were occasionally subjected to force perturbations. We find that motor adaptation was specifically impaired on the most difficult to categorize trials. Conclusions/Significance We suggest that the degree of decision-level difficulty of a particular categorization differentially burdens the executive system and subsequently results in a proportional degradation of adaptation. Our results suggest a specific quantitative contribution of executive control in motor adaptation.

Weakly Circadian Cells Improve Resynchrony

The mammalian suprachiasmatic nuclei (SCN) contain thousands of neurons capable of generating near 24-h rhythms. When isolated from their network, SCN neurons exhibit a range of oscillatory phenotypes: sustained or damping oscillations, or arrhythmic patterns. The implications of this variability are unknown. Experimentally, we found that cells within SCN explants recover from pharmacologically-induced desynchrony by re-establishing rhythmicity and synchrony in waves, independent of their intrinsic circadian period We therefore hypothesized that a cells location within the network may also critically determine its resynchronization. To test this, we employed a deterministic, mechanistic model of circadian oscillators where we could independently control cell-intrinsic and network-connectivity parameters. We found that small changes in key parameters produced the full range of oscillatory phenotypes seen in biological cells, including similar distributions of period, amplitude and ability to cycle. The model also predicted that weaker oscillators could adjust their phase more readily than stronger oscillators. Using these model cells we explored potential biological consequences of their number and placement within the network. We found that the population synchronized to a higher degree when weak oscillators were at highly connected nodes within the network. A mathematically independent phase-amplitude model reproduced these findings. Thus, small differences in cell-intrinsic parameters contribute to large changes in the oscillatory ability of a cell, but the location of weak oscillators within the network also critically shapes the degree of synchronization for the population.

Beside the point: motor adaptation without feedback-based error correction in task-irrelevant conditions.

Adaptation of movement may be driven by the difference between planned and actual motor performance, or the difference between expected and actual sensory consequences of movement. To identify how the nervous system differentially uses these signals, we asked: does motor adaptation occur when movement errors are irrelevant to the task goal? Participants reached on a digitizing tablet from a fixed start location to one of three targets: a point, an arc, or a ray. For the arc, reaches could be in any direction, but to a specific extent. For the ray, reaches could be to any distance, but in a targeted direction. After baseline reaching to the point, the direction or extent of continuous visual feedback was perturbed during training with either a cursor rotation or gain, respectively, while reaching to either the ray (goal = direction) or the arc (goal = extent). The perturbation, therefore, was either relevant or irrelevant to the task goal, depending on target type. During interspersed catch trials, the perturbation was removed and the target switched back to the point, identical to baseline. Although the goal of baseline and catch trials was the same, significant aftereffects in catch trials indicated behavioral adaptation in response to the perturbation. Adaptation occurred regardless of whether the perturbation was relevant to the task, and it was independent of feedback control. The presence of adaptation orthogonal to task demands supports the hypothesis that the nervous system can rely on sensory prediction to drive motor learning that can generalize across tasks.

Trial-by-trial motor adaptation: a window into elemental neural computation.

How does the brain compute? To address this question, mathematical modelers, neurophysiologists, and psychophysicists have sought behaviors that provide evidence of specific neural computations. Human motor behavior consists of several such computations [Shadmehr, R., Wise, S.P. (2005). MIT Press: Cambridge, MA], such as the transformation of a sensory input to a motor output. The motor system is also capable of learning new transformations to produce novel outputs; humans have the remarkable ability to alter their motor output to adapt to changes in their own bodies and the environment [Wolpert, D.M., Ghahramani, Z. (2000). Nat. Neurosci., 3: 1212-1217]. These changes can be long term, through growth and changing body proportions, or short term, through changes in the external environment. Here we focus on trial-by-trial adaptation, the transformation of individually sensed movements into incremental updates of adaptive control. These investigations have the promise of revealing important basic principles of motor control and ultimately guiding a new understanding of the neuronal correlates of motor behaviors.

Learning and Memory Formation of Arm Movements

Learning a motor task is characterized by a gradual transition from a high demand on attention to the task becoming automatic and nonattentive. Studies that have recorded limb movements during learning of a motor task have shown that this increase in automaticity of movements is accompanied by key kinematic features: 1 Stiffness of the limbs decreases (Milner and Cloutier 1993), as evidenced by a decreased coactivation of the muscles and an increased compliance in response to a perturbation. 2 Movements become smoother (Hreljac 1993), as evidenced by a reduction in a cost function that scales with the jerkiness of the movement (second derivative of velocity). 3 Motion of the joints become decoupled (Vereijken et al. 1992), as evidenced by a reduction in the cross-correlation between patterns of joint rotations.

Training scientists in a science center improves science communication to the public

the language of science is inherently academic and often inefficient in its delivery of key concepts to nonscientists (10a). When President Obama spoke at the National Academy of Sciences in April 2009 (15b), he issued the following call to think of new ways to engage young people in science,

Environmental experience within and across testing days determines the strength of human visuomotor adaptation

The use of vision allows us to guide and modify our movements by appropriately transforming external sensory information into proper motor commands. We investigated how people learned visuomotor transformations in different visual feedback environments. These environments presented perturbations of visual sense of movement direction. Across experiments and testing days, we altered the likelihood of visual perturbation occurrence and the distribution of sign and strength of visual perturbation angles. We then observed how transformation of sensed error into incremental adaptation depended on visual perturbation angle and on environmental experience. We found that environmental context affected adaptive responses within a day and across days. The across-day effect was profound enough that people exhibited very weak or very strong adaptive sensitivity to identical stimuli, dependent solely on prior days’ experience. We conclude that trial-by-trial adaptation to visual feedback is not fixed, but dependent on environmental experiences on both short and long time scales.