Keynote Addresses

Join us at the Society for the Neural Control of Movement Annual Meeting to hear the Distinguished Career Award Winner and the Early Career Award Winner deliver keynote presentations.

Distinguished Career Award Winner Presentation

2024 Distinguished Career Award Winner

Friday, April 19: 17:00 – 18:00

Eberhard Fetz

Eberhard Fetz

University of Washington

Eberhard Fetz received his Ph.D. in physics from the Massachusetts Institute of Technology in 1967.  He is currently Professor emeritus in the Department of Physiology & Biophysics and DXARTS at the University of Washington.

His overall research has concerned the neural control of limb movement in primates and mechanisms of neural computation. His initial studies concerned monkeys’ ability to volitionally control the activity of cortical cells.  In this operant conditioning paradigm monkeys drove a biofeedback meter arm to reinforcement threshold with different patterns of activity in motor cortex neurons.  This work in 1969 showed that neural activity could be used to drive an external device, the precursor of current brain-machine interfaces. He went on to document the correlational linkages and response properties of premotorneuronal cells in motor cortex and spinal cord.  His lab pioneered the recording of spinal interneurons in behaving monkeys and showed that spinal neurons share many properties of cortical cells, including preparatory activity prior to instructed movements. The synaptic interactions between cortical neurons were investigated with in vivo intracellular recordings and spike-triggered averages of membrane potentials. Dynamic recurrent neural network models were used to elucidate population mechanisms generating behavior like target tracking and short-term memory.  Most recently, his lab developed a head-fixed bidirectional brain-computer interface that can record activity of cortical neurons during free behavior and convert this activity in real time to stimulation of cortex, spinal cord, or intracranial reward sites.  Closed-loop activity-dependent stimulation produced changes in the strength of synaptic connections; the underlying mechanisms were captured in spiking neural network models.  His extracurricular interests include artistic ways to represent the operations of the brain.  For more details see the lab website:


On the “Neural Control of Movement”

Assuming that the neural control of movement poses answerable questions, we have investigated the relations between primate motor cortex cells and muscles using diverse approaches. First, we trained monkeys to activate motor cortex cells, hoping to reveal their “muscle fields”; this quickly proved to be untenable because monkeys could make many different movements (or none) to fire any cell.  Moreover, even consistently correlated cells and muscles could be readily dissociated by operant conditioning.  Focusing next on those cells whose spikes causally facilitate muscles (probably via monosynaptic corticomotoneuronal connections) we discovered a variety of relationships in their relative firing patterns.  Remarkably, monkeys could even dissociate the activity of CM cells from their target muscles, in both directions (still unpublished), showing that the relative activation of these directly connected elements is surprisingly flexible. The explicit coding of movement parameters by populations of cortical cells turns out to be slippery as well: many different parameters can be extracted from the same population of cells with simple linear decoders.  Trajectories of population activity in multidimensional neural space have led to the concept of lower-dimensional manifolds constraining dynamics, suggesting that explanations of movement control are better sought in the properties of manifolds than the roles of individual neurons.  Such descriptive exercises are conceptually seductive but evade the harder details about how neural computation in the brain causally generates volitional movements. Toward that end, experiments combining operant conditioning with multiunit recording and neural network modeling could provide further insights.

Early Career Award Winner Presentation

2024 Early Career Award Winner

Tuesday, April 16: 10:30 – 11:05

Sam McDougle

Sam McDougle

Yale University

Samuel McDougle is an assistant professor of Psychology at Yale University, where he runs the Action, Computation, and Thinking (ACT) Lab. He and his team investigate the intersection of cognition and motor control, using computational, behavioral, and neuroimaging techniques. Dr. McDougle earned his BA in Neuroscience at Vassar College before going on to work as a research technician at UPenn studying sensorimotor learning in mice. He earned a PhD in Psychology and Neuroscience at Princeton University, researching motor learning in humans. He then completed a postdoc at UC Berkeley where he studied cognitive influences on human reinforcement learning and the role of the cerebellum in nonmotor behaviors. The ACT lab opened at Yale in 2020 and is working to bridge the fields of cognitive psychology and motor neuroscience by revealing basic algorithms of the mind and brain. Current projects explore some of the following questions: How do high-level intentions shape eventual motor outputs? How do different learning contexts influence the acquisition of novel motor skills? How might the brain’s sensorimotor circuits contribute to cognition? And how do executive functions like attention and working memory interact with the motor system?


“Cognitive Shaping of Motor Behavior”


The fields of motor neuroscience and cognitive psychology are too often siloed. But cognitive processes affect motor behavior in a range of ways, influencing the selection, planning, and learning of movements. In turn, how we move affects what we perceive, closing the loop between cognitive and motor systems. In this talk, I will discuss some recent projects from my lab that highlight the intersection of cognition and motor behavior. I will feature work on how cognitive stages of action planning shape implicit forms of motor learning, the dynamic flow of information from decision-making to movement selection systems, and neural computations that cut across action and visual cognition. Overall, I will try to make the case that studying motor behavior in a vacuum risks missing key stops along the road from thought to action.



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