Dendritic, Neuronal & Circuit Modelling
We build computational models of various abstraction levels (single cell, microcircuit and network level) and brain regions (hippocampus, amygdala, prefrontal cortex, visual cortex) and use them to study specific functions: neural computation, learning and memory, interneuron function, spatial navigation/learning, decision making, sensory perception, focusing on dendrites. Our models are often used in conjunction with experiments from collaborating labs.
Experimental / Behavioral Neuroscience
We use head-fixed behavioral set-ups, in vivo imaging and electrophysiology to probe dendritic contributions to neural activity and behavior. Current projects focus on the role of dendritic nonlinearities in the prefrontal cortex and the animal’s ability to exhibit behavioral flexibility.
Machine Learning / Artificial Intelligence
We transfer key findings from our dendritic models (plasticity rules, activation functions) to machine learning, in particular deep learning algorithms. We are interested in understanding how dendritic properties may advance the state of the art in machine learning algorithms, and vice-versa, whether machine learning can explain neural computations. We also develop machine learning tools for neuroscience data analysis.