Research Overview

Classical approaches to motor systems research (and much of neuroscience) involve measuring the properties of single neurons (Hubel, 1957) and characterizing their responses during complex behaviour. However, with the advent of more sophisticated recording techniques, such as floating microelectrode arrays and optical imaging (O'Shea et al. 2017), we can probe the dynamics of large neural populations by recording thousands, or even 10s of thousands (Pachitariu et al. 2016), of neurons in parallel.

Using these techniques to understand how reaching movements are controlled, we can move away from the neuron doctrine — which posits that the neuron is the functional and perceptual unit of the nervous system, and towards a network view — in which ensembles of distributed neurons form functional units with their own emergent properties.

Publications

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.

Education

Georg-August-Universität Göttingen, Germany

Dr. rer. nat. (Systems Neuroscience) 2017

Queen's University, Canada

Bachelor of Science (Honours) 2011

Positions

Neural Prosthetic Systems Lab, Stanford

Postdoctoral Fellow May, 2017 - Present

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
Clark Center
318 Campus Drive
Stanford, CA 94305
USA

650-451-2363
JMichaels (at) stanford.edu









Upcoming Presentations

A modular neural network model of the primate grasping circuit (nanosymposium).

46th Annual Meeting of the Society for Neuroscience. Washington DC November 14th, 2017





Quotes

  • 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