Understanding how the brain constructs and simulates world models is a fundamental challenge in neuroscience. We are looking for someone to join our crew to explore the neuronal circuit mechanisms for learning and simulating world models. This project aims to explore the neuronal circuit mechanisms underlying how cortical networks learn and simulate world models, focusing on how multimodal sensory information and motor feedback shape predictive models in the brain. Drawing on principles from deep generative models, reinforcement learning (RL), and self-supervised learning, we aim to bridge the gap between AI and neuroscience by studying how cortical circuits build internal models to guide perception, control, and decision-making. This project will leverage computational modeling and the analysis of large-scale neural recordings during optogenetic manipulations to investigate how cortical networks integrate sensory inputs, predict future states, and use these predictions to guide behavior.

Key Objectives:

• Investigate how cortical circuits learn predictive models of the world, integrating multimodal sensory information with motor feedback.
• Develop computational models that simulate the brain’s ability to generate predictions and actions using self-supervised learning and reinforcement learning principles.
• Validate these models using large-scale electrophysiological recordings from behaving animals.

The findings of this project will advance our understanding of the biological basis of intelligence and inform the development of more advanced AI systems, especially in the realm of self-supervised learning, model-predictive control, and reinforcement learning.

Role: Postdoc or PhD student

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