RESEARCH 2018-06-21T19:32:48+00:00

Our Research

The endogenous cannabinoid signaling network has been shown to be involved in natural physiological processes such as pain perception, thermoregulation and motor coordination. The system is also implicated in maladaptive motivated behaviors such as drug addiction and obesity. Our research seeks to extract neurobiological correlates of certain behaviors, within specifically defined anatomical frameworks to understand how key neural circuits in the brain function.

We use advanced electrophysiological and neurochemical techniques to examine the activity patterns of individual neurons as well as neuronal populations and how these are regulated via neurotransmitters (dopamine in particular). We have recently implemented the use of a sensor that allows for the simultaneous measurement of neuronal firing and neurotransmitter release. We complement this approach with modern neuroscience techniques such as opto and chemogenetics as well as calcium imaging in freely moving animals using mini-endoscope technology. These techniques are particularly useful for elucidating specific temporal relationships between behavior and brain activity.

Techniques

  • Optogenetics
  • Chemogenetics
  • Iontophoresis
  • Intra-cranial self-stimulation
  • Drug self-administration
  • Genetically encoded calcium indicators
  • Calcium imaging neuron microscopy
  • Fast-scan cyclic voltammetry (FSCV)
  • Ensemble (multiple single-unit) recordings
  • Combined FSCV and single-unit electrophysiology

Projects

Neural network dynamics in association with the onset and progression of Huntington’s Disease in a transgenic mouse model.

Endocannabinoid modulation of goal-directed behavior.

Endocannabinoid modulation of learning behavior through process of negative reinforcement.

The role of cannabinoid receptor activity in aversive learning behavior (in collaboration with Carl Lupica).

Serotonin/dopamine interactions (in collaboration with Marisela Morales).

The role of subsecond dopamine release in observational learning (in collaboration with Matt Roesch).