Current Environment:

Computational Neuroscience Laboratory Research | Overview


Led by Caterina Stamoulis, PhD, our laboratory is involved in a wide range of studies in Computational Neuroscience. We develop novel signal processing, mathematical and statistical methods and models with the goal to characterize brain dynamics and networks from high-dimensional brain data. Research in the laboratory lies at the intersection of Neuroscience and Data Science and aims to address fundamental questions in Cognitive and Systems Neuroscience using Big Data.

We are specifically interested in the development of the human brain’s functional circuitry from infancy to young adulthood. The overarching goal of our work is to characterize the (re)organization of brain network topologies and dynamics to support increasingly complex cognitive skills and efficient processing of the outside world, using mathematical/statistical approaches and models. We are particularly interested in how the developing brain responds to its environment in ecologically-valid settings, to process complex multimodal sensory information, and how its neural circuitry is modulated by neurodevelopmental/neurological disorders and stressors.

In the fall 2019 we are starting a new collaborative study (supported by the National Science Foundation) focusing on the impact of individual functional connectomes on group behaviors, with an overarching goal to bridge Neuroscience and the Social Sciences through methodological innovations in Data Science.

Research in the laboratory is supported by the large-scale BRAIN and Harnessing the Data Revolution Initiatives through the National Science Foundation, the National Institutes of Health and institutional/internal sources.

Current projects


  • Dynamic characterization of functional networks in the developing human brain in ecologically-valid settings
  • Multi-modal (unconstrained) sensory information processing in incompletely maturated and sub-optimally parsimonious neuronal networks
  • Modulation of neuronal network dynamics by repeated sleep restriction and recovery
  • Brain-heart Interactions in the sleep-deprived brain
  • The neural basis of collective behaviors (from the individual to the collective brain).
  • Novel electrophysiological markers of seizure dynamics in the epileptic brain
  • Multi-modal data integration of improved seizure characterization and classification
  • Improved source localization in the presence of spatial anisotropy
  • Optimization of nuclear imaging modalities for improved seizure localization
  • Neural and cardiovascular dynamics in the epileptic brain


  • Improved weak signal detection in spatially correlated noise and low signal-to-noise ratio
  • High-dimensional modularity estimation and classification in multi-scale networks
  • Multi-scale dimensionality reduction in very high-dimensional correlated datasets
  • Time series models of neural dynamics
  • Robust detection of stochastic activity in noisy circuits
  • Models ofoptimization of time-varying network topologies for efficient processing


  • Novel signal processing approaches for improved analysis of high-dimensional genomic data
  • NGNDA: Novel computing infrastructure for massive analyses of high-dimensional brain networks and next-generation research in the Neurosciences