Our research involves interdisciplinary collaborations with neuroscience labs and research groups:
Nair Lab – MU College of Engineering
Sah Lab – Queensland University, Australia
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:
Neural oscillations - Automated identification of oscillations using in vivo LFP data for detect oscillations online in experiments; Modeling gamma and its modulation in the amygdala; Beta-gamma coupling in network models of M1 cortex; Mechanisms underlying the generation of spontaneous ripples in amygdala and hippocampus; Automated prediction of oscillatory bursts in vivo using LFP data
Single neuron and network models - Integration of dendritic spikes in a morphologically detailed single cell model; Machine learning models for automated identification of neuron types from neuropixel probe data; How do neurons and networks maintain steady output in the presence of variability in both intrinsic and synaptic properties?
Amygdala and fear - Role of NMDARs in generating network activity during the processing of auditory tones and on plasticity during fear conditioning and extinction;
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.
Computational Neuroscience: Models & Neurobiology (for pre-doc, post-docs, medical students, residents and faculty)
Research Experiences for Teachers
Six-week research experience for K-12 teachers.
See CyNeuro.org for open-source research and teaching software hosted by the Lab