Exploring the limits of behavioral complexity in rats: a novel experimental approach via reinforcement learning and information theory

• Applicant: Dr. Johannes Niediek
• Subject Area: Cognitive, Systems and Behavioural Neurobiology ; Bioinformatics and Theoretical Biology
• Term: from 2020 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442068558

Neuroscientists are ultimately interested in the neural processes that enable animals and humans to carry out complex behaviors in the wild. However, animals in laboratories are usually studied in experimental environments with heavily restricted behavioral freedom. In many rat experiments, for instance, rats can either push a lever or not, or choose between two possible levers – and nothing else. A main reason for heavily restricting behavior in animal experiments has been a lack of methods for the design, modeling, and analysis of experiments with unrestricted behavior.The goal of this project is to adapt methods from the theory of reinforcement learning to model and analyze the behavior of unrestricted rats and to apply these new methods to a novel class of behavioral experiments in real rats.An attempt to realistically model unrestricted behavior should take into account that animals have limited neural resources. For example, a rat might be unable to precisely remember the shortest possible path to a remote food source (the optimal behavior given unlimited neural resources), but might be able to reach the food source on some longer path (optimal behavior given limited neural resources). I will combine the mathematical theory of reinforcement learning with the notion of information constraints from information theory to model the behavior of animals with prescribed limits on resources. The novel behavioral paradigms introduced here encourage rats to increase the complexity of their behavior over time: the difficulty of these adaptive tasks automatically increases whenever the rat performs well, and decreases whenever the rat performs poorly.The modeling methods I propose will allow to predict rat behavior with prescribed amounts of neural resources in tasks with (almost) unrestricted behavioral freedom. Running the proposed adaptive tasks with real rats will enable me to investigate the limits of complexity that rats can handle and their preferred complexity levels. Lastly, I will record from auditory neurons during behavior in order to search for neural correlates of behavioral complexity and task difficulty.

• DFG Programme: Research Fellowships
• International Connection: Israel
• Host: Professor Israel Nelken, Ph.D.