Research Overview

Mundane activities such as turning a doorknob or picking up a cup are completely effortless for healthy humans. Yet, the precision of skilled human behaviour can be dizzyingly complicated, such as playing a piano concerto or performing a gymnastics routine. My goal is to understand how humans are able to flexibly produce this fantastic range of behaviour, from simple arm movements to complex finger dexterity. To understand motor control, my main approach is to combine deep learning models of complex behavior and the electrophysiological study of population of neurons in the primate brain.

Classical approaches to motor systems research (and much of neuroscience) involve measuring the properties of single neurons during behaviour. However, with the advent of more sophisticated recording techniques, we can probe the dynamics of large neural populations by recording 10s of thousands (Steinmetz et al. 2019) of neurons. At the same time, deep learning has emerged as a powerful tool for reproducing human-level skills during some complex behaviours, such as the game of Go (Silver et al. 2018) and Starcraft (Vinyal et al. 2019). Critically, deep learning has driven fundamental advancements in our understanding of the brain in the visual (Yamins et al. 2014; Bashivan, Kar, & DiCarlo 2019; Kar et al. 2019) and motor systems (Sussillo et al. 2015; Pandarinath et al. 2018). While success in reproducing human-like reaching and grasping behavior has improved drastically (OpenAI et al. 2019), we require a much greater fundamental understanding of motor control before we can match human-level learning and performance.


Michaels JA, Schaffelhofer S, Agudelo-Toro A, Scherberger H (2020). A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping. Proceedings of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.2005087117.

Codol O, Ariani G, Michaels JA (2020). Aiming for stable control. Nature Neuroscience, 23(3), 298-300. doi:10.1038/s41593-020-0601-2.

Intveld RW, Dann B, Michaels JA, Scherberger H (2018). Neural coding of intended and executed grasp force in macaque areas AIP, F5, and M1. Scientific Reports, 8(17985). doi:10.1038/s41598-018-35488-z.

Michaels JA*, Dann B*, Intveld RW, Scherberger H (2018). Neural dynamics of variable grasp movement preparation in the macaque fronto-parietal network. Journal of Neuroscience, 38(25), 5759-5773. doi:10.1523/JNEUROSCI.2557-17.2018.

Michaels JA, Scherberger H (2018). Population coding of grasp and laterality-related information in the macaque fronto-parietal network. Scientific Reports, 8(1710). doi:10.1038/s41598-018-20051-7.

Michaels JA, Dann B, Scherberger H (2016). Neural population dynamics during reaching are better explained by a dynamical system than representational tuning. PLOS Computational Biology, 12(11), e1005175. doi:10.1371/journal.pcbi.1005175.

Michaels JA, Scherberger H (2016). hebbRNN: A reward-modulated Hebbian learning rule for recurrent neural networks. The Journal of Open Source Software. doi:10.21105/joss.00060. pdf.

Dann B, Michaels JA, Schaffelhofer S, Scherberger H (2016). Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates. eLife. doi:10.7554/eLife.15719.

Dann B, Michaels JA, Scherberger H (2016). Separable decoding of cue, intention, and movement information from the fronto-parietal grasping-network. Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, 218. doi:10.3217/978-3-85125-467-9.

Michaels JA, Dann B, Intveld RW, Scherberger H (2015). Predicting reaction time from the neural state space of the premotor and parietal grasping network. Journal of Neuroscience, 35(32), 11415–11432. doi:10.1523/JNEUROSCI.1714-15.2015.

Yang L, Michaels JA, Pruszynski JA, Scott SH (2011). Rapid motor responses quickly integrate visuospatial task constraints. Experimental Brain Research, 211(2): 231-242. doi:10.1007/s00221-011-2674-3.


Georg-August-Universität Göttingen, Germany

Dr. rer. nat. (Systems Neuroscience) 2017

Queen's University, Canada

Bachelor of Science (Honours) 2011


Pruszynski Lab, Brain and Mind Institute, London, Ontario, Canada

Postdoctoral Fellow June, 2019 - Present

Neural Prosthetic Systems Lab, Stanford

Postdoctoral Fellow May, 2017 - June, 2019

Neurobiology Lab, German Primate Center

Transitional Postdoctoral Fellow January, 2017 - May, 2017

Neurobiology Lab, German Primate Center

Ph.D. Student September, 2011 - January, 2017

Neuroplasticity Lab, Queen's University

Bachelor Student September, 2010 - June, 2011

Laboratory of Integrative Motor Behaviour, Queen's University

Undergraduate Researcher May, 2009 - August, 2011

Language and Cognition Lab, Queen's University

Research Assistant September, 2008 - May, 2009

Contact Details

Jonathan Michaels
Brain and Mind Institute
Western Interdisciplinary Research Building
London, ON, N6A 3K7

jonathan.michaels (at)

Upcoming Presentations

A modular neural network model of grasp movement generation

COSYNE 2020 Workshops Breckenridge, CO March 3rd, 2020


  • An epic, twenty-year battle was fought over the cortical representation of movement. Do motor cortex neurons represent the direction of the hand during reaching, or do they represent other features of movement such as joint rotation or muscle output? As vigorous as this debate may have been, it still did not address the nature of the network within the motor cortex. Indeed, it tended to emphasize the properties of individual neurons rather than network properties....The battles over the cortical representation of movement never satisfactorily addressed those questions.

    Michael Graziano (2011). New insights into motor cortex. Neuron 71:387–88
  • Neurophysiological experiments have revealed neural correlates of many arm movement parameters, ranging from the spatial kinematics of hand path trajectories to muscle activation patterns. However, there is still no broad consensus on the role of the motor cortex in the control of voluntary movement. The answer to that question will depend as much on further theoretical insights into the computational architecture of the motor system as on the design of the definitive neurophysiological experiment.

    John Kalaska (2009). From intention to action: motor cortex and the control of reaching movements. Adv. Exp. Med. Biol. 629:139–78
  • A shift in how to examine the motor system occurred in the 1980s from a problem of control back to a problem of what variables were coded in the activity of neurons. . . . [P]erhaps it is time to re-evaluate what we are learning about M1 function from continuing to ask what coordinate frames or neural representations can be found in M1. Perhaps it is time to stop pursuing the penultimate goal of identifying the coordinate frame(s) represented in the discharge patterns of M1 and again move back to the question of control.

    Stephen Scott (2008). Inconvenient truths about neural processing in primary motor cortex. J. Physiol. 586:1217–24

Picture Credits

Picture Credits

Cortex Drawing - Localization of motor hand area to a knob on the precentral gyrus: A new landmark. Brain, 120, 141-157.
Cover Image - Dr. Katie Kelly, Johns Hopkins University / Dr. Laura Schrader, Tulane University