Our research is an interdisciplinary collaboration between the following labs and research groups:
- Nair Lab – MU College of Engineering
- Kalivas Lab – Medical University of South Carolina
- Pare Lab – Rutgers University
- Quirk Lab – University of Puerto Rico School of Medicine
- Schulz Lab – MU Biological Sciences
Systems and Computational Neuroscience
The Nair lab projects involve reverse engineering the brain circuits in invertebrates and vertebrates, at intracellular, cellular and systems levels, in close association with neuroscientists and biologists. We model a neuron as a nonlinear electrical circuit and combine many neurons to form networks. Using biologically realistic and reduced order models, we use system theoretic concepts to investigate how such neurons/network circuits implement functions. We study neurocomputational and system level issues such as bifurcation, adaptation and learning (LTP/LTD, etc.), robustness, control and related ones for these nonlinear dynamic circuits.
We are presently studying the following:
- (i) how do neurons and networks maintain steady output in the presence of variability in both intrinsic and synaptic properties
- (ii) how are conditioning and extinction fear memories acquired and stored in the amygdala and the associated cortical structures? How does context modulate fear and extinction?, and
- (iii) what are the neuroplasticity mechanisms that might explain known cellular adaptations due to cocaine in the PFC-NAc glutamatergic pathway? What are possible mechanisms of LTP/LTD in the accumbal PSD?
Biologically realistic modeling of neural circuits will provide a fundamental understanding of the underlying brain mechanisms in both health and disease (e.g., PTSD, anxiety disorders, neuroplasticity due to cocaine). Such models will also help lay the groundwork for innovative pharmacological, psychotherapeutic and other treatments by permitting rapid in-computer experimentation.