I am interested in computer modeling of neurons and neuronal networks.

How information flows and is processed in the brain is extremely fascinating. Neurons are the key players in this process because of their unique ability, among all cell types of the body, to propagate signals rapidly over big distances. They do it by generating characteristic electrical pulses (“action potentials” or “spikes”) that travel down nerve fibers. Neurons represent and transmit information by firing sequences of spikes.

A lot is known about the biophysical mechanisms responsible for generating and regulating neuronal activity, and about synaptic connectivity between neurons. This knowledge provides basis for constructing mathematical models of neurons ranging from detailed descriptions involving many coupled differential equations to greatly simplified ones useful for studying large interconnected networks.

Computer simulations of neurons and neural networks are complimentary to traditional techniques in neuroscience. They enrich and often guide experimental approaches.

My ongoing collaborative projects include:

  • Multisensory integration in thalamocortical neuronal network – in collaboration with Army Research Laboratory Aberdeen Proving Ground – funded by the Fisher College for Science and Mathematics General Endowment Grant 

Using an adaptation of the previously published Single-Column Thalamocortical Network Model “Traub model” (Traub et al, J Neurophys 2005 Apr, 93(4):2194-232) adapted by ARL group to be used in the GENESIS neuronal simulation environment, we are trying to shed light on the mechanism of nonlinear integration of presynaptic inputs in individual neurons in cortex, on small scale network level and in a large thalamocortical network.

  • Effects of heterogeneity of neuronal electrical properties on the stability of the intrinsic dynamics of the cortical network -in collaboration with Army Research Laboratory Aberdeen Proving Ground

Heterogeneity of neurons in the biological brain is thought to contribute positively to the brain’s ability to support the stability of its intrinsic dynamics. We operationally define stability as resistance to the transition from a low-activity state to high-activity state. The aperiodic low-activity state can be thought of as characterizing the healthy brain activity when sensory inputs are able to alter the brain’s ongoing dynamics to generate relevant changes in behavior. In contrast, the high-activity state involves persistent periodic firing like that in a critical pathological state (for example, a seizure). In the high activity state, the brain would be insensitive to changes in sensory input and unable to respond adaptively. We introduce heterogeneity to the intrinsic biophysical parameters of the neurons in the thalamo-cortical network model developed by our ARL collaborators and investigate the effects of this heterogeneity on the stability of the network, and do this for the models with different levels of connectivity in the network.

Despite the facts that nicotine is addictive and smoking has been associated with risk of lung cancer, the intake of nicotine through smoking is known to boost concentration and provide relaxation. A number of studies indicate that nicotine improves such cognitive functions as learning and memory. Furthermore, nicotine and other nicotinic agents have been shown to provide neuroprotection and reduce cognitive decline associated with Alzheimer’s disease, Parkinson’s disease, schizophrenia, Autism spectrum disorders, brain trauma and aging.

When nicotine enters the bloodstream it crosses the blood-brain barrier and reaches the brain, where it binds to and activates nicotinic acetylcholine receptors (nAChRs). While these receptors have been extensively studied, the precise mechanism of how activation of nAChRs relates to changes in brain network dynamics remains elusive.

Using NEURON modeling software, we construct, optimize and test models of neurons that include nAChRs. Then we connect these neurons in a small biophysically constrained network and investigate the effects of nAchRs activation and modulation on the network level.