The field of neuroscience is rapidly evolving as emerging technologies enable recording of activity from thousands of neurons in awake and behaving animals. However, analysing the large amounts of data generated by these experiments is challenging from both technical and conceptual perspectives. Our research focuses on the development and application of computational methods to understand patterns and structures in complex datasets of neural activity and behaviour to investigate information processing in the brain. To achieve this, we employ a wide range of techniques from artificial intelligence, machine learning, mathematics, statistics and physics.
Current research areas include:
- Discovering the topological structures and dynamical properties of large-scale neural activity and sensory coding through neural manifold estimation and nonlinear time series analysis.
- Investigating information processing in the brain through the lens of complex systems via the analysis of functional connectivity networks and network dynamics.
- Tracking and modelling sequences of animal behaviour to study the algorithmic basis of sensory processing and action selection.