Vasiliauskaite, Vaiva Dr.

Student Projects

If you are a student seeking for a project on computational neuroscience, do not hesitate to get in touch. I am looking for motivated students interested in developing computational modelling and data analysis frameworks for bottom-up neuroscience.

Research Interests

One of the central challenges in neuroscience is understanding how the brain computes information. Some of the questions that drive this field are: How much information does a single neuron or a network of neurons encode and transmit? How is this information integrated to support functions and behaviors? How does network structure influence information processing, and how might disease alter this complex dynamical system?

A significant obstacle in advancing these questions is the challenge of learning from empirical data. Over the past decades, new technologies have helped scientists to generate a vast amount of biological data, such as electrophysiological recordings using high density micro-electrode arrays that reflects on system's behavior at a high spatio-temporal resolution. However, mathematical models often rely on non-observable variables or idealized data that are impractical or impossible to observe directly. Furthermore, making sense of the data generated by a neuronal system is not easy from the phenomenological point of view either. As complex systems, neural circuits often exhibit network effects (where the network structure shapes computation patterns), as well as emergence (where collective behaviors arise from local interactions in a non-linear way).

Despite these challenges, computational models are crucial as they offer an efficient way to predict experimental outcomes, and serve as a foundation for developing theories of brain function. My work seeks to bridge the gap between empirical data and computational modeling. This approach requires that models be constructed in a way that allows for experimental validation. To achieve this, I draw on various modeling and analysis modalities, including but not limited to:

• Forward modeling, i.e. building biophysically plausible models of extracellular recording of a neuron or their population;
• Inverse modeling, i.e. extracting governing equations from data through the use of AI;
• Complex systems and network science, i.e. quantifying connectivity patterns and how they shape neuronal dynamics;
• Multivariate information theory, i.e. quantifying information flow within distributed neural circuits over time, assessing how information is encoded or transferred, quantifying extent of redundancy and synergy in the system.

Vasiliauskaite, V., & Antulov-Fantulin, N. (2024). Generalization of neural network models for complex network dynamics. Communications Physics, 7(1), 348.
Vasiliauskaite, V., & Hausladen, C. I. (2023). How do circadian rhythms and neural synchrony shape networked cooperation?. Frontiers in Physics, 11, 1125270.
Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: a gentle introduction. ECHO doi.org/10.47041/CVTD4629