We are looking for a fearless new lab member to investigate the neuronal circuit mechanisms for strategic planning. Strategic planning is a hallmark of intelligent behavior. Squirrels bury nuts to prepare for winter, crows store tools for future use, and mice can navigate mazes by remembering how to get to the food. However, the neuronal mechanisms that support these behaviors remain poorly understood. This project aims to investigate how cortical circuits implement strategic planning by leveraging insights from deep reinforcement learning (RL). We will develop deep RL agents capable of complex planning tasks and use these models to explore the underlying computational signatures. We will search for these signatures in neural data from behaving animals, examining how the brain encodes strategic decisions in neural population activity. This project will combine computational modeling, deep learning, and neural data analysis to reveal how cortical circuits orchestrate complex behaviors.

We seek candidates with a quantitative background who are passionate about neuroscience and computation. Candidates should be skilled in analytical and computational tools, including (but not limited to) deep reinforcement learning, control theory, and dynamical systems. Creativity and the ability to combine approaches from different domains will be critical. Ideal candidates will be curious about the neural basis of cognition and decision-making while enjoying challenging math and coding problems.

Key Objectives:

• Develop deep RL agents to simulate strategic planning and decision-making.
• Explore different network architectures and their suitability for learning predictive models to evaluate future actions.
• Investigate the representational geometry in trained RL agents and compare these representations to neural population activity recorded from animals performing similar tasks.
• Propose optogenetic manipulation experiments in behaving animals to explore how cortical circuits encode and support planning.

This project aims to deepen our understanding of how the brain supports strategic behavior by combining advanced deep-learning approaches with in-vivo data analysis. Insights gained from these studies will illuminate fundamental mechanisms of cognition and improve the design of artificial agents capable of planning and decision-making in real-world scenarios.

Role: Postdoc or PhD student

For additional details see https://zenkelab.org/jobs/