Latest publication investigates the network mechanisms behind the spread of task-related activity in local circuits, using theoretical models and data analysis to study the hypothesis that a small subset of neurons in the cortex actively engages in learning tasks, while the rest of the neurons are driven through pre-existing synaptic pathways.
Research Experience
Focused on uncovering the organizational principles that govern neuron connectivity (structure) and their impact on neural activity (dynamics), as well as investigating how learning shapes the brain's structure and dynamics to adapt and generate complex behaviors. Aiming to develop new theories and models of brain circuits by combining theoretical insights, data-driven modeling, and close collaboration with experimentalists.
Background
Research interests include theoretical and computational neuroscience, using a wide range of tools from various fields such as statistical mechanics, dynamical systems theory, machine learning, and control theory to explore how cognitive abilities emerge from collective neural activity and how such activity evolves during the learning process.
Miscellany
Looking for motivated postdocs and students with a strong background in Computational Neuroscience, Physics, and Machine Learning who are eager to understand brain functions.