Publications by authors named "Max Berniker"

Article Synopsis
  • - Formalizing surgical activities as triplets of instruments, actions, and target anatomies helps enhance the understanding of tool-tissue interactions, improving AI assistance in image-guided surgeries.
  • - The CholecTriplet2022 challenge expands the previous work by adding weakly-supervised localization of surgical tools and modeling their activities as ‹instrument, verb, target› triplets.
  • - The paper outlines a baseline method and presents 10 new deep learning algorithms, while also comparing their effectiveness and analyzing results to provide insights for future surgical research.
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When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy.

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Reaches in experimental settings are commonly found to be straight. This straightness is robust to physical, but not visual, perturbations. Here, we question whether typical visual feedback contributes to this finding by implicitly promoting straight movements.

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Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as objective performance indicators, OPIs) computed during key surgical tasks correlate with surgeon skill and clinical outcomes. Unambiguous identification of these surgical tasks can be particularly challenging for both human annotators and algorithms.

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When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation.

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Behavioral studies consistently find that subjects move their hand along straight paths despite considerations that suggest reaches should be curved. Literature on this topic makes it clear that the experimentally displayed feedback influences how subjects reach. Could the standard visual feedback, a displayed cursor, explain the lack of path curvature in experimental results? To address this question, we conducted three experiments to examine reach behavior in the absence of the standard visual feedback.

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Subjects in laboratory settings exhibit straight hand paths-typified by the minimum jerk path-even in the presence of a learned but disturbing force field. At the same time it is known that in this setting, visual feedback strongly influences reaches, biasing them to be straight. Here we examine whether or not this bias can account for the straightness of movements made in a force field.

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While we can readily observe and model the dynamics of our limbs, analyzing the neurons that drive movement is not nearly as straightforward. As a result, their role in motor behavior (e.g.

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How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation).

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The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time.

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The two-alternative forced-choice (2AFC) task is the workhorse of psychophysics and is used to measure the just-noticeable difference, generally assumed to accurately quantify sensory precision. However, this assumption is not true for all mechanisms of decision making. Here we derive the behavioral predictions for two popular mechanisms, sampling and maximum a posteriori, and examine how they affect the outcome of the 2AFC task.

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To generate new movements, we have to generalize what we have learned from previously practiced movements. An important question, therefore, is how the breadth of training affects generalization: does practicing a broad or narrow range of movements lead to better generalization? We address this question with a force field learning experiment. One group adapted while making many reaches in a small region (narrow group), and another group adapted while making reaches in a large region (broad group).

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Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.

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Most approaches to understanding human motor control assume that people maximize their rewards while minimizing their motor efforts. This tradeoff between potential rewards and a sense of effort is quantified with a cost function. While the rewards can change across tasks, our sense of effort is assumed to remain constant and characterize how the nervous system organizes motor control.

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Functional electrical stimulation (FES) attempts to restore motor behaviors to paralyzed limbs by electrically stimulating nerves and/or muscles. This restoration of behavior requires specifying commands to a large number of muscles, each making an independent contribution to the ongoing behavior. Efforts to develop FES systems in humans have generally been limited to preprogrammed, fixed muscle activation patterns.

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Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance.

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The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control.

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Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors.

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The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a low-dimensional controller and an underlying principle for its creation has remained elusive.

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Motor adaptation is usually defined as the process by which our nervous system produces accurate movements while the properties of our bodies and our environment continuously change. Many experimental and theoretical studies have characterized this process by assuming that the nervous system uses internal models to compensate for motor errors. Here we extend these approaches and construct a probabilistic model that not only compensates for motor errors but estimates the sources of these errors.

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The human ankle varies impedance and delivers net positive work during the stance period of walking. In contrast, commercially available ankle-foot prostheses are passive during stance, causing many clinical problems for transtibial amputees, including non-symmetric gait patterns, higher gait metabolism, and poorer shock absorption. In this investigation, we develop and evaluate a myoelectric-driven, finite state controller for a powered ankle-foot prosthesis that modulates both impedance and power output during stance.

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