Professional Education


  • Harvard University (2008)
  • Doctor of Philosophy, Stanford University, NEURS-PHD (2014)
  • Bachelor of Arts, Harvard University, Engineering (Biomedical) (2008)

Stanford Advisors


All Publications


  • Cerebellar granule cells encode the expectation of reward NATURE Wagner, M. J., Kim, T. H., Savall, J., Schnitzer, M. J., Luo, L. 2017; 544 (7648): 96-?

    Abstract

    The human brain contains approximately 60 billion cerebellar granule cells, which outnumber all other brain neurons combined. Classical theories posit that a large, diverse population of granule cells allows for highly detailed representations of sensorimotor context, enabling downstream Purkinje cells to sense fine contextual changes. Although evidence suggests a role for the cerebellum in cognition, granule cells are known to encode only sensory and motor context. Here, using two-photon calcium imaging in behaving mice, we show that granule cells convey information about the expectation of reward. Mice initiated voluntary forelimb movements for delayed sugar-water reward. Some granule cells responded preferentially to reward or reward omission, whereas others selectively encoded reward anticipation. Reward responses were not restricted to forelimb movement, as a Pavlovian task evoked similar responses. Compared to predictable rewards, unexpected rewards elicited markedly different granule cell activity despite identical stimuli and licking responses. In both tasks, reward signals were widespread throughout multiple cerebellar lobules. Tracking the same granule cells over several days of learning revealed that cells with reward-anticipating responses emerged from those that responded at the start of learning to reward delivery, whereas reward-omission responses grew stronger as learning progressed. The discovery of predictive, non-sensorimotor encoding in granule cells is a major departure from the current understanding of these neurons and markedly enriches the contextual information available to postsynaptic Purkinje cells, with important implications for cognitive processing in the cerebellum.

    View details for DOI 10.1038/nature21726

    View details for Web of Science ID 000398323300040

    View details for PubMedID 28321129

  • Imaging neural spiking in brain tissue using FRET-opsin protein voltage sensors NATURE COMMUNICATIONS Gong, Y., Wagner, M. J., Li, J. Z., Schnitzer, M. J. 2014; 5

    View details for DOI 10.1038/ncomms4674

    View details for Web of Science ID 000335221800005

    View details for PubMedID 24755708

  • Imaging neural spiking in brain tissue using FRET-opsin protein voltage sensors. Nature communications Gong, Y., Wagner, M. J., Zhong Li, J., Schnitzer, M. J. 2014; 5: 3674-?

    Abstract

    Genetically encoded fluorescence voltage sensors offer the possibility of directly visualizing neural spiking dynamics in cells targeted by their genetic class or connectivity. Sensors of this class have generally suffered performance-limiting tradeoffs between modest brightness, sluggish kinetics and limited signalling dynamic range in response to action potentials. Here we describe sensors that use fluorescence resonance energy transfer (FRET) to combine the rapid kinetics and substantial voltage-dependence of rhodopsin family voltage-sensing domains with the brightness of genetically engineered protein fluorophores. These FRET-opsin sensors significantly improve upon the spike detection fidelity offered by the genetically encoded voltage sensor, Arclight, while offering faster kinetics and higher brightness. Using FRET-opsin sensors we imaged neural spiking and sub-threshold membrane voltage dynamics in cultured neurons and in pyramidal cells within neocortical tissue slices. In live mice, rates and optical waveforms of cerebellar Purkinje neurons' dendritic voltage transients matched expectations for these cells' dendritic spikes.

    View details for DOI 10.1038/ncomms4674

    View details for PubMedID 24755708

  • Shared Internal Models for Feedforward and Feedback Control JOURNAL OF NEUROSCIENCE Wagner, M. J., Smith, M. A. 2008; 28 (42): 10663-10673

    Abstract

    A child often learns to ride a bicycle in the driveway, free of unforeseen obstacles. Yet when she first rides in the street, we hope that if a car suddenly pulls out in front of her, she will combine her innate goal of avoiding an accident with her learned knowledge of the bicycle, and steer away or brake. In general, when we train to perform a new motor task, our learning is most robust if it updates the rules of online error correction to reflect the rules and goals of the new task. Here we provide direct evidence that, after a new feedforward motor adaptation, motor feedback responses to unanticipated errors become precisely task appropriate, even when such errors were never experienced during training. To study this ability, we asked how, if at all, do online responses to occasional, unanticipated force pulses during reaching arm movements change after adapting to altered arm dynamics? Specifically, do they change in a task-appropriate manner? In our task, subjects learned novel velocity-dependent dynamics. However, occasional force-pulse perturbations produced unanticipated changes in velocity. Therefore, after adaptation, task-appropriate responses to unanticipated pulses should compensate corresponding changes in velocity-dependent dynamics. We found that after adaptation, pulse responses precisely compensated these changes, although they were never trained to do so. These results provide evidence for a smart feedback controller which automatically produces responses specific to the learned dynamics of the current task. To accomplish this, the neural processes underlying feedback control must (1) be capable of accurate real-time state prediction for velocity via a forward model and (2) have access to recently learned changes in internal models of limb dynamics.

    View details for DOI 10.1523/JNEUROSCI.5479-07.2008

    View details for Web of Science ID 000260060600021

    View details for PubMedID 18923042

  • Spaticitemporal linear decoding of brain state IEEE SIGNAL PROCESSING MAGAZINE Parra, L. C., Christoforou, C., Gerson, A. D., Dyrholm, M., Luo, A., Wagner, M., Philiastides, M. G., Sajda, P. 2008; 25 (1): 107-115