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.
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.
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.
Dr. rer. nat. (Systems Neuroscience)• 2017
Bachelor of Science (Honours)• 2011
Postdoctoral Fellow • May, 2017 - Present
Transitional Postdoctoral Fellow • January, 2017 - May, 2017
Ph.D. Student • September, 2011 - January, 2017
Bachelor Student • September, 2010 - June, 2011
Undergraduate Researcher • May, 2009 - August, 2011
Research Assistant • September, 2008 - May, 2009
318 Campus Drive
Stanford, CA 94305
JMichaels (at) stanford.edu
March 2nd, 2018
March 5th, 2018
March 6th, 2018
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
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