Ruben A. Tikidji-Hamburyan, Ph.D.
- Senior Research Scientist
- Department of Physiology & Pharmacology
- School of Medicine and Health Sciences,
- George Washington University
- Washington, DC 20052, USA
-
: rath@gwu.edu || rth@r-a-r.org || ruben.tikidji.hamburyan@gmail.com
Read about my research in
by American Institute of Physics
FIELD OF RESEARCH:
Computational and Theoretical NeuroscienceSCIENTIFIC INTERESTS:
- Information processing and information coding in neural networks.
- Detailed biophysical models of local neural networks. Analysis of neural network dynamics influenced by single neuron dynamical properties (afterpolarization, bursting, postinhibitory rebound, etc.).
- Neuronal mechanisms of sensory perception.
- Formation and stabilization of neuronal assemblies. Development of network connectivity. Synaptic plasticity, spines, back propagated action potential, calcium, and biochemical kinetics.
My overall goal is to look at information processing through the (lens) of biological neurons and networks, combining modelling with experimental verification. Detailed biophysical models allow us to replicate this (lens) with great accuracy, theoretical analyses help us to understand how a specific network performs as an information machine, and experimental verification ensures and refines the relevance of this work.
KEY ACHIEVEMENTS:
- Novel computational frameworks to study a visual map alignment in the superior colliculus during a critical period of development. The frameworks accurately model many aspects of the process in vivo. These results suggest that the specific and precise cortico-subcortical and cortico-cortical connectivity pattern can be segregated from other connectivity by unstable manifolds.
- A simple mechanism of cross-frequency coupling which can explain phase-to-phase coupling and phase-to-amplitude coupling (including theta/gamma nesting in the hippocampus). This mechanism was verified by direct experiments in hippocampal CA1 pyramidal neurons.
- A new mechanism of gamma oscillations that exhibits robust synchronization under conditions of noise and heterogeneity in synaptic connectivity, resonance frequency, and axonal conduction delays, regardless of whether individual neurons are biased in mean or fluctuation-driven regimes. This mechanism was verified by direct experiments in entorhinal cortical, PV-positive basket interneurons.
- A fundamentally new mechanism for the detection of Interaural Time Differences (ITD). The model revealed how a relatively large population of slowly integrating neurons can detect microsecond time differences in input signals that are significantly shorter than any time constant in this system
SELECTED PUBLICATIONS
- Tikidji-Hamburyan R.A., Govindaiah G., Guido W., Colonnese M.T.(2023) Synaptic and circuit mechanisms prevent detrimentally precise correlation in the developing mammalian visual system, eLife 12:e84333 [code][doi][database]
- Tikidji-Hamburyan R.A., El-Ghazawi T.A., Triplett J.W.(2016) Novel Models of Visual Topographic Map Alignment in the Superior Colliculus , PLoS Comput Biol 12(12):e1005315 [code][doi]
- Tikidji-Hamburyan R.A., Martínez J.J., White J.A., Canavier C.C.(2015) Resonant Interneurons Can Increase Robustness of Gamma Oscillations, J Neuroscience 35(47):15682-15695 [code][doi]
- Vasilkov V. A. and Tikidji-Hamburyan R.A.(2012) Accurate Detection of Interaural Time Differences by a Population of Slowly Integrating Neurons, Phys. Rev. Lett. 108:138104 [code][doi]
SELECTED WORKSHOPS, SEMINARS, ORAL PRESENTATIONS
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Resonant Interneurons Can Increase Robustness Of Gamma Oscillations.,
2015, UT Austin Conference on Learning & Memory, Austin TX -
Microsecond Accuracy in Detection of Interaural Time Differences by Population of Sluggish and Noisy Integrating Neurons: Why Do We Need Large-Scale Network Simulations and Neuroscience Gateway Portal.,
2014, Workshop of Neuroscience Gateway Portal, Washington D.C. -
The Necessary and Sufficient Conditions for Population Coding of Interaural Time Differences by Trough-Type Neurons.,
2010, Seminars at the Center for Neural Science (Prof. J. Rinzel’s lab), New York University, NY | the Department of Neuroscience, University of Connecticut Health Center, Farmington, CT