We are interested in understanding how the brain exerts cognitive control over actions. Currently, we ask the following questions:
Neural circuits generating learned movements
How does the brain learn and perform motor actions? Towards this end, we train rats to perform a simple reaction time task (cf. Laubach et al., 2000) and carry out behavioral analysis, lesion, and neurophysiology recordings to pinpoint key nodes in the brain that enable the animal to initiate, hold, and terminate an action. We have some exciting results revealing the functions of cortical and subcortical structures in this behavior.
Neural circuits mediating inhibitory control
To wait before acting in daily life requires the brain's inhibitory control over action when the timing is inappropriate. In rodents, previous studies have demonstrated that such cognitive demand requires the medial prefrontal cortex (Narayanan et al., 2006; Risterucci et al., 2003). We are following up on previous work by addressing how the activity of mPFC neurons facilitates waiting or suppresses premature action. In particular, we wish to elucidate the neural circuits linking cognitive control and movement. Currently, we are using chemogenetic method to inactivate frontal cortex at different phases during learning.
Neural circuits underlying BMI
Volitional control of neural activity is the very basis of brain machine interface. We have developed a (relatively) simple paradigm in which rats volitionally modulate neuronal spike rates to move a drinking port. This system allows us to probe the neural circuits producing volitional signals.
Object representation and memory
How do animals discriminate and recognize objects, for example, use tactile sensing? Inspired by Mumby box (Mumby DG et al., 1990), we developed a semi-automatic system to study object discrimination and memory. This system can flexibly accommodate defined behavioral rules (e.g., delayed non-matching-to-sample). Our long-term goal is to investigate the representation of objects in sensory and associative cortex, as well as subcortical regions. We are currently working to fully automate the system to make it compatible with physiological recordings and high-throughput behavioral training.