Harvard University (2008)
Doctor of Philosophy, Stanford University, NEURS-PHD (2014)
Bachelor of Arts, Harvard University, Engineering (Biomedical) (2008)
Liqun Luo, Postdoctoral Faculty Sponsor
- Imaging neural spiking in brain tissue using FRET-opsin protein voltage sensors NATURE COMMUNICATIONS 2014; 5
Imaging neural spiking in brain tissue using FRET-opsin protein voltage sensors.
2014; 5: 3674-?
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
2008; 28 (42): 10663-10673
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 2008; 25 (1): 107-115