Topic: motor control

How does a cat gracefully walk and suddenly freeze when spotting a mouse? In this talk, we look at how networks in the spinal cord generate movement. In particular, we address the fundamental yet poorly understood question of motor control: How can rhythmic movements, such as walking, be generated and stopped at any point in the cycle while posture is preserved? Since conventional models of spinal motor function rely on alternation between flexor and extensor modules, which are limited to just two phases, this question exposes the essential shortcoming of the conventional understanding: How ca

Many animals rely on persistent internal representations of continuous angular variables for working memory, motor control, and navigation. Theories have proposed that such representations are maintained by a class of recurrently connected networks called ring attractor networks. These networks rely on large numbers of neurons to maintain continuous and stable representations and to accurately integrate incoming signals. The head direction system of the fruit fly, however, seems to achieve these properties with a remarkably small network. These findings challenge our understanding of ring attr

I will discuss work with Jack Lindsey modeling reinforcement learning for action selection in the basal ganglia. I will argue that the presence of multiple brain regions, in addition to the basal ganglia, that contribute to motor control motivates the need for an off-policy basal ganglia learning algorithm. I will then describe a biological implementation of such an algorithm that predicts tuning of dopamine neurons to a quantity we call "action surprise," in addition to reward prediction error. In the same model, an implementation of learning from a motor efference copy also predicts a novel

Animals from flying insects to running humans thrive in uncertain and noisy environments. They smoothly navigate, swiftly track targets and escape predators because their sensorimotor systems are precisely fine-tuned for these tasks. Millisecond-precise spike timing codes, prevalent in both sensory and motor systems, can prompt large changes in muscle forces especially when several muscles coordinate to control a biomechanical action. Such coordinated spike timing changes, modulated by task-relevant sensory encoding over long timescales, can significantly contribute to the effectiveness of mot

One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales in order to efficiently shape structured behaviors. I will present research investigating how these complex computations are performed in the mammalian brain, with an emphasis on autonomous motor control. Specifically, I will focus on the mechanisms supporting efficient interfacing between 'higher-level' planning commands and 'lower-level' motor cortical dynamics that ultimately drive muscles. I will take advantage of the fact that the anatomy of the circuits underlying mot

This course provides a fundamental foundation in the modern techniques of experimental neuroscience. It introduces the essentials of sensors, motor control, microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot. In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.

Analogies are widely used by teachers and coaches of different movement disciplines, serving a role during the learning phase of a new skill, and honing one’s performance to a competitive level. In previous studies, analogies improved motor control in various tasks and across age groups. Our study aimed to evaluate the efficacy of analogies throughout the learning process, using kinematic measures for an in-depth analysis. We tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. The

Join the Department of Bioengineering on the 26th May at 9:00am for The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium. This year’s lecture speaker will be distinguished bioengineer and neuroscientist Professor Mandyam V. Srinivasan AM FRS, from the University of Queensland. Professor Srinivasan studies visual systems, particularly those of bees and birds. His research has revealed how flying insects negotiate narrow gaps, regulate the height and speed of flight, estimate distance flown, and orchestrate smooth landings. Apart from enhancing funda

The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk w

Animals control motor actions at multiple timescales. We use larval zebrafish and advanced optical microscopy to understand the underlying neural mechanisms. First, we examined the mechanisms of short-term motor learning by using whole-brain neural activity imaging. We found that the 5-HT system integrates the sensory outcome of actions and determines future motor patterns. Second, we established a method for recording spiking activity and membrane potential from a population of neurons during behavior. We identified putative motor command signals and internal copy signals that encode millisec

fMRI results have shown that the supplementary motor area (SMA) and the basal ganglia, most often discussed in their roles in generating action, are engaged by beat-based timing even in the absence of movement. Some have argued that the motor system is “recruited” by beat-based timing tasks due to the presence of motor-like timescales, but a deeper understanding of the roles of these motor structures is lacking. Reviewing a body of motor neurophysiology literature and drawing on the “active inference” framework, I argue that we can see the motor and timing functions of these brain areas as exa

Physics offers countless examples for which theoretical predictions are astonishingly powerful. But it’s hard to imagine a similar precision in complex systems where the number and interdependencies between components simply prohibits a first-principles approach, look no further than the challenge of the billions of neurons and trillions of connections within our own brains. In such settings how do we even identify the important theoretical questions? We describe a systems-scale perspective in which we integrate information theory, dynamical systems and statistical physics to extract understan

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of

Today, considerable information is available on the organization and operation of the neural networks that generate the motor output for animal locomotion, such as swimming, walking, or flying. In recent years, the question of which neural mechanisms are responsible for task-specific and flexible adaptations of locomotor patterns has gained increased attention in the field of motor control. I will report on advances we made with respect to this topic for walking in insects, i.e. the leg muscle control system of phasmids and fruit flies. I will present insights into the neural basis of speed co

This is the inaugural symposium of the Simons-Emory International Consortium on Motor Control, and speakers will deliver 10 minute talks (each followed by 10 minutes of discussion) addressing this question: "What tools are we missing to understand motor control, and what could we learn if we had them?”

Motor control is a complex, high-dimensional task. It has been suggested that the brain may reduce the dimensionality of this control problem by using a set of motor primitives (or muscle synergies) that encode frequently used patterns of muscle co-activations in the time and/or weight-domain (Bizzi et al., 1991; Kutch and Valero-Cuevas, 2012; Bizzi and Cheung, 2013; Alessandro et al., 2013). In this project we investigate how motor primitives may affect the learning of movement in motor cortex. We model motor cortex as a recurrent neural network (RNN) and compare two potential output scenario

For motor control, the activities of local neural nets need to be coordinated, which can be attained via direct synaptic connections. Phylogenetically, the main purpose of the brain is the active interaction with the environment which suggests the presence of an alternative neural coordination mechanism, namely one that is mediated by a feedback loop involving the outside world. In this context we study the coordination of otherwise isolated neural clusters via 'force coupling', viz exclusively through the reactive forces of the body. Coordination arises when a given neural net reacts to the

Brain activity lies on a very complex network. This complexity is multiple but a prominent one is the non-linear and multi-factorial interaction between its nodes. In the specific case of Parkinson's disease (PD), the brain loses dopaminergic neurons in the substantia nigra compacta (SNc). Highly involved in the basal ganglia-cortex-thalamus-cerebellum (BG-Ctx-T-Cb) network, the main role of dopamine (DA) is in learning, decision-making and motor control, as manifested in the PD symptoms such as rigidity, slowness of movements, tremor and poor behavioural flexibility. Following DA loss, the un

The human brain quickly adapts to a changing environment, often faster than can be explained by synaptic plasticity. One of the main candidates to account for such rapid changes is neuromodulation. We explore how modulation can assist a recurrent neural network to accommodate a range of variations of the tasks it is initially trained on. There are a number of approaches allowing for rapid acquisition of new tasks, for example, the neuronal gain modulation model [1]. However, we found that by affecting the input-output function of a neuron such an approach can neither address the variation of a

Flour beetles such as Tribolium castaneum are devastating pests of dry stored grain products, causing up to 19% loss of these commodities in the Global South. Little is known about how the nervous systems of these animals coordinate effective movement. We therefore employed novel behavioural assays combined with whole-animal and leg tracking to assess how larvae move over different types of terrain. We found that larvae move most efficiently over fibrous substrates (e.g. paper), consistent with the beetle’s original habitat of tree bark. Locomotion over flat terrain primarily involves movement

Goal-directed action control relies on the prefrontal cortex (PFC), where distinct subsections collaborate in functional networks. The extent to which the network favors a hierarchical system, characterized by early convergence within the PFC, versus a shallow brain hypothesis, featuring numerous parallel circuits from the cortex to downstream targets, remains elusive (Suzuki et al. 2023). Employing optogenetic modulation, behavioral assessments, and fMRI, we investigate the impact of PFC subarea-stimulation on motor control and whole-brain activation patterns. We quantified error rate and rea