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Alexey Kuznetsov

Available immediately are several projects combining state of the art statistical analysis and biophysical modeling of brain electrical activity. Specifically, in collaboration with the IUPUI psychology department, we record from the brain structures that are responsible for learning and affected by addictive drugs. We use classical and develop new statistical methods to analyze the time series of the electrical activity recorded. Further, we design biophysical models that simulate the recorded activity and allow us to discover and test its generating mechanisms and functional significance. The models connect single neurons into local circuits and further into multiple brain regions. We directly test the influence of addictive drugs in the simulations and guide further experiments by generating predictions. The scientific direction is currently supported by the National Institute of Alcohol Abuse and Alcoholism (NIAAA).


  • Postdoctoral Research Associate, Boston University, Mathematics Department, Center for BioDynamics, 1/02 - 7/05
  • PhD equivalent in Physics and Mathematics, State University of Nizhny Novgorod, Nizhny Novgorod, Russia, 9/1996-6/1999

Courses Taught / Teaching

I teach and coordinate the Ordianary Differential Equation class (MATH 26600) as well as Qualitative Theory of Differential Equations (MATH 52200). Further, I taught mumerous other classes, including Calculus I-II, Linear Algebra and Numerical Analysys.


My research focuses on designing computational and mathematical models for biological processes. Two types of the processes I am currently studying are electrical activity of neurons and dynamics of regulatory molecular networks. A mathematical aim of my research is to develop common modeling approaches that can be used for analysis of a wide range of systems. Other aims are specific for every project and depend on the biological system I model. Currently, the major direction of my research is mathematical modeling of how addictions change the brain circuitry and function. I am designing a model of the brain circuitry that is responsible for goal-directed learning and hijacked in addictions - the dopamine system and its feedback mechanisms. The project offers an exceptional opportunity to connect experiments at different levels by modeling and achieve deep understanding of how specific addictive drugs targets produce its system level effects.

Publications & Professional Activities

E. Morozova, D. Zakharov, B. Gutkin, C. Lapish, A. Kuznetsov; Dopamine neurons change the type of excitability in response to stimuli. PLoS Computational Biology 12(12): e1005233  https://doi.org/10.731/journal.pcbi.1005233 .

E. Morozova, M. Myroshnichenko, M. di Volo, B. Gutkin, C. Lapish, A. Kuznetsov; Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting. Accepted to J. Neurophysiology, 2016.

C. C. Canavier, R. C. Evans, A. M. Oster, E. K. Pissadaki, G. Drion, A. S. Kuznetsov, B. S. Gutkin; Implications of cellular models of dopamine neurons for disease. Journal of Neurophysiology Aug 2016, jn.00530.2016; DOI: 10.1152/jn.00530.2016

D. Zakharov, C. Lapish, B. Gutkin, A Kuznetsov; Synergy of AMPA and NMDA receptor currents in dopaminergic neurons: a modeling study. Frontiers in Computational Neuroscience Special Issue "Burst coding: from Cell to Cognition", 10: 48. 2016 doi:  10.3389/fncom.2016.00048. BioArchive doi: http://dx.doi.org/10.1101/024653

D. Zakharov, A Kuznetsov; On the Dynamical Mechanism of the Influence of Synaptic Currents on the Neuron Model with Response Differentiation. Letters to Journal of Experimental and Theoretical Physics (JETP Letters). V. 102 N.3 PP. 184-188, 2015.

A. Kuznetsov and B. Gutkin, Dopaminergic Cell Models. In Encyclopedia of Computational Neuroscience, Springer, DOI 10.1007/978-1-4614-7320-6_86-2, 2014.

D. Fu, P. Tan, A. Kuznetsov, Y. Molkov, Chaos and Robustness in a Single Family of Genetic Oscillatory Networks. PLoS ONE 9(3): e90666. doi:10.1371/journal.pone.0090666 2014.

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