Nicolas Diekmann

This study shows that the cerebellum and ventral tegmental area interact during fear extinction learning, supporting a role for prediction-error processing in this process.

This paper uses change-point analysis to show that reversal learning is more difficult than initial learning, as evidenced by later change points and hippocampus-dependent replay of previous experiences.

This study uses 6 Hz cerebellar transcranial alternating current stimulation to enhance fear extinction learning by modulating cerebellar-cortical interactions in healthy participants.

This study examines the effects of dopaminergic drugs on fear extinction learning in humans, finding small effects primarily in the bromocriptine group.

This paper introduces a stochastic replay mechanism for reinforcement learning agents that improves spatial learning performance by prioritizing experiences based on strength, similarity, and inhibition of return.

CoBeL-RL is a simulation framework for modeling complex behavior and learning in neuroscience, offering virtual environments and RL algorithms for efficient simulation and analysis.

This study develops a reinforcement learning model that learns context-dependent extinction behavior from raw visual inputs, showing that context dependence can emerge without explicit context representation.