Topic: temporal contexts

Learning enables animals to construct flexible knowledge structures, allowing them to adapt to new tasks by reconfiguring prior experiences into novel strategies. This compositional nature of learning suggests that fundamental building blocks from past experiences are essential for future problem-solving. To investigate how experience shapes learning, we trained Recurrent Neural Networks (RNNs) to model a complex, context-dependent odor timing task, using constraints derived from prior reports detailing mouse behavior and shaping procedures. The task, a temporal variant of the Delay Non-match