About his postdoctoral project

Self-localization based on internal and/or external sensory information is essential to animal survival. The hippocampus plays a crucial role in this process by forming a mental map of the environment. The factors influencing internal spatial coding resolution are poorly understood. Recent large-scale in vivo recordings of hippocampal neuronal activity in mice navigating virtual reality environments show local variations in spatial coding resolution. They could result from genuine differences within a single internal map or possible switches between different maps with different spatial resolutions. To solve this issue, we propose to use inference models based on functional connectivity (Ising models) as a neural activity decoder to decipher at high speed the neuronal assemblies or maps in use. Map switching will be revealed as instabilities (or flickers). In a second step, we will build an attractor neuronal network model fed by both external and internal information to investigate mechanisms of spatial resolution modulation.