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.

Publications

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.