Ruben A. Tikidji-Hamburyan, Ph.D.

Read about my research in

Physics News Highlights

by American Institute of Physics


Computational and Theoretical Neuroscience


  • 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.


  • 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