Keywords: Ca2+ imaging, mouse, behavior, neuron, networks, correlation
Project partners: Prof. Flavio Donato, Dr. Catalin Mitelut, Dr. Steffen Kandler
CeDA collaborator: Rodrigo C. G. Pena
Repository: neural-graphs-mice-development


The brain's ability to support sophisticated cognitive function like learning, memory, navigation, and decision making relies on the interaction of thousands of neurons that change their activity over time according to dynamics that are ultimately related to what an animal is experiencing on a moment-to-moment basis. At the level of individual neurons, activity dynamics integrate signals coming from the sensory world with self-generated activity patterns that largely result from the topology of the network in which these neurons are embedded. At the neuronal network level, the temporal progression of such population dynamics keeps track of:

  • the temporal unfolding of sensory stimuli impinging on the animal,
  • the progressive changes in behaviorally-relevant variables like, for example, the animal's position in space or distance to goals,
  • dynamic changes in the animal's internal states, and
  • an internal model that predicts the outcome of the animal's actions.

In the past years it has become evident therefore, that to understand how populations of neurons support cognitive functions it is increasingly important to describe emergent phenomena that modulate the propagation of activity in biological neuronal networks, and how they relate to an animal's behavior.

Project objectives

The aim of this collaboration is to study how the structures of neural connections evolve during the development of mice. The dataset contains close-to-daily recordings of mice on a treadmill. The development stage of the mice in the dataset varies. The recordings are two-photon calcium fluorescence images, which are then processed to yield correlation-based neuronal similarity measures. Neuronal networks are derived from these similarity measures and we try to quantify how these networks change with regards to developmental stage.


  • Define neuronal networks from correlation-based measures
  • Compare the structure of these networks throughout development via summaries or other means
  • Test for significant differences and interpret what the differences mean