Neural circuits that control reward learning and movement

Joining the lab

I am looking for students from neuroscience or engineering backgrounds, who are interested in studying neural circuits that control reward learning or movement. Learning to code in Matlab to analyze data will be a major part of the training experience. To help familiarize students with analysis of time-varying data I co-teach Neuro 260: Introduction to Signal Processing for Neuroscientists, a graduate elective course that combines lectures and coding tutorials. I keep a fairly small lab (3-6 trainees) as I find that that size range allows me to provide the most effective mentoring. Previous trainees have gone onto a variety of careers including academia and biotech industry. We are big supporters of open-source technology and collaborate with labs on campus and beyond.

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 areas we work on are summarized below.

1. Dynamics of corticostriatal circuits during reward-conditioned or self-initiated movement

Our signature technology is a silicon 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.

2. Contribution of various sources of input to neural dynamics in the striatum

In addition to studying what information is encoded by neural dynamics, we are interested in how dynamics of neural populations in the striatum are generated as a result of various local and external inputs. To address this we combine neural recordings with optogenetic perturbations.

Here we evaluated the role of parvalbumin-expressing striatum interneurons on projection neuron activity and behavior.

3. Contribution of corticostriatal circuits to reward-conditioned behavior

We examine the causal role of specific brain circuits on behavior using perturbations to activate or silence neurons or their inputs.

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

4. Disruption of neural activity and information processing in brain disorder models

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