The brain has a remarkable capacity to learn continuously about the environment and to use this knowledge flexibly to make predictions and guide its future decisions. Our group studies learning and memory from computational, algorithmic/representational and neurobiological viewpoints.
Internal representations of the environment are key to interpret novel environmental stimuli. Our group combines machine learning, cognitive science, and neuroscience to understand how these internal models are built, used, and updated while interacting with the environment.
The lab focuses on how humans and animals learn to make sense of their visual environment based on their momentary sensory input and their internal representation of earlier short- and long-term experiences.
We are using mathematical models and computational analysis to study the neuronal basis of memory and navigation. Our goal is to understand how basic biophysical mechanisms in a specific neuronal system (the hippocampus) give rise to higher order cognitive processes. What is the effect of nonlinear dendritic processing of inputs on the dynamics of the network and thus how they influence the learning and recall of memories and ultimately the behavior of the animal?