Studying the dynamics of brains and behavior
My lab has two main research objectives.
- The first is to further our understanding of how multiple decision-making (learning and memory) systems interact to produce behavior.
- The second is to apply the theories that arise from the neurophysiology and computational modeling to explain dysfunctional and broken behavioral-control systems.
To meet these objectives, the lab combines multi-electrode neural ensemble recordings from awake, behaving animals with complex computational analysis techniques that enable measurement of neural dynamics at very fast time scales (e.g. msec). The lab also builds computational models at all scales (single-neuron compartmental models to large-scale systemic models to abstract algorithmic models) to connect the multiple levels of neurophysiology and behavior.
Modern neuroscience sees the brain as an information-processing device. Understanding how the brain processes information requires understanding the representations used by the network of neurons that compose the brain. However, representations in the brain are distributed: each cell carries only a small portion of the total information. I am interested in questions of how neural structures work together to create systems able to accomplish behavioral tasks.
More specifically, we have ongoing projects in
- the dynamics of neural ensemble activity in multiple systems (hippocampus, dorsal striatum, ventral striatum, amygdala, prefrontal cortex, orbitofrontal cortex) during learning,
- the interaction between multiple learning systems (such as hippocampus and striatum) in the ability to accomplish complex tasks,
- computational models of addiction and other mental disorders.