About the lab



Joining the lab

I would love to hear from students from neuroscience or engineering backgrounds, who have a passion for learning more about neural circuits that control reward learning or movement. No previous coding experience is required, although learning to code in Matlab in order to analyze data will be a major part of the training experience. I like to keep a fairly small lab (3-6 trainees) as I find that that size range allows me to provide the most effective mentoring. All desk and office space, including my office, are found right inside the main lab, so I'm easy to reach for help and discussion. New students initially team up with a senior member of the lab, and gain more independence as they master the experimental and analytical techniques. A typical day in the lab may involve some of the following activities: performing experiments to record or manipulate brain activity, training mice on a certain task, surgical procedures, analyzing the data, writing up the findings, and holding discussions with other team members. Students are strongly encouraged to apply to training grant programs, and to attend at least one major neuroscience conference each year. Previous trainees have gone onto a variety of careers including academia and biotech industry.


Research interests

Learning and movement are fundamental functions of the brain, yet many aspects of how these processes are orchestrated by various circuits remain elusive. Our group is addressing several open questions about the neurobiological basis of learning and movement in health and disease. Some key questions we work on are summarized below.


1. What are the dynamics of neural circuits during reward-conditioned behavior?


Our signature technology is a probe for recording the electrical activity of large populations of neurons. We rely on these tools to study the dynamics of cortical and basal ganglia circuits in behaving mice. Most of our work is carried out in mice trained on Pavlovian reward conditioning tasks, but we also work with operant tasks. Lab members learn how to perform experiments with these recording tools (as well as complementary methods such as optogenetics and fiber photometry), and analyze data to examine dynamics and information processing in different cell types and brain regions. This effort allows frequent interactions with a vibrant community of UCLA researchers interested in computational and systems-level neuroscience.


Here we studied the temporal processing properties of corticostriatal network dynamics.


Here we compared neural activity in the orbitofrontal cortex and striatum during a two-choice action selection task.


2. What role does the activity of specific brain circuits play in reward-conditioned behavior?


We examine the causal role of specific brain circuits in behavior using perturbative methods such as optogenetics, chemogenetics, and pharmacology to activate or silence neurons or their inputs. This approach is highly synergistic with our effort to understand the dynamics of neural circuits using advanced recording tools.


Here we evaluated the role of parvalbumin-expressing striatal interneurons in Pavlovian conditioning.


Here we compared the contribution of dopaminergic neurons to associative learning versus online movement generation.


3. How is neural activity and information processing disrupted in models of brain disorders?


We use large-scale neural recordings to identify aberrant patterns of neural activity in models of disorders such as addiction, Parkinson's, and Huntington's disease. Our work primarily focuses on activity in the striatum (including nucleus accumbens), and its cortical inputs. This is a highly collaborative effort, relying on frequent interactions with other labs at UCLA with expertise in brain disorder models.


Here we found correlations between frontostriatal network dynamics and drug cue-evoked arousal.


Here is a review on the main ways in which neural activity in the striatum is altered in models of Parkinson's disease.