OptoRoboRat I

a fiber-based calcium imaging system with combined movement tracking and decoding approach

Relevant for Research Area

A - Foundations

C - Applications

The project builds on

DeepDecode, BIG


Prof. Ilka Diester

Prof. Marlene Bartos

Prof. Thomas Brox


Within this project we will establish a fiber-based imaging system. The calcium imaging data obtained will be processed with a movement tracking system (Zimmermann et al, 2020). This should enable us to predict movements via decoding of neural data (Melbaum et al, 2020). It is a technically challenging, but as from the preliminary data and manuscripts, already existing preparatory work on two components, namelythe movement tracking and decoding of neural data and themotion prediction. In the past, however, decoding was done usingelectrophysiological data, not calcium measurements. This represents adecisive step in the realization of the research program at the OptoRoboRatand the securing of the expertise gained within DeepDecode.

Research Status

The project OptoRoboRat I aims at establishing a fiber based imaging system that is compatible with head-fixed 2-photon imaging and holographical stimulation. This would allow a direct comparison of neuronal activity during freely moving and restraint behavior. During the previous funding period methodologically, we have successfully established the 1-photon calcium imaging in freely moving mice using the fiber based imaging system. Simultaneously, we automatically tracked the animals’ movement with the newly developed 3D tracking tool FreiPose (Schneider et al, in prep). Further, we combined neuronal and behavioral data with a novel reinforcement based approach to predict behavior based on neural responses (Kalweit et al., 2021). Currently, we are working on the next milestone: A behavioral paradigm that can be employed in both freely moving in a real environment and head-restraint mice moving in virtual realities. The task has already been designed and is now being implemented. The next step will be to refine the training protocol and to collect data that allows us to predict the behavior based on the calcium imaging signals.



Karvat, G., Schneider, A., Alyahyay, M., Steenbergen, F., Tangermann, M., & Diester, I. (2020). Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback. Communications biology, 3(1), 1-10.

Kalweit, G., Kalweit, M., Alyahyay, M., Jaeckel, Z., Steenbergen, F., Hardung, S., Diester, I., & Boedecker, J. (July 24th, Saturday, 2021). NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding. [Conference Presentation], ICML-CompBio-2021, poster at virtual conference