Finally, a deep neural network model trained with gradient descent and dopamine-like teaching signals captured the mice's learning trajectories from naive to expert.

Remarkably, the model's fixed-point graph succinctly explained the diverse yet systematic strategies mice developed through learning.

Dopamine (DA) signals in the dorsolateral striatum (DLS) provided further evidence for deep GD learning.

DLS DA acted as a partial stimulus-based RPE that only drove learning for stimuli used in decisions ("associated"), resembling the dependence of GD updates on hidden-layer representations.

We found evidence for deep GD dynamics in mice learning a task from naive to expert:

1. Learning transitioned through strategies that persisted for several days
2. From early behavior, we could predict behavior many days later
3. Strategies developed sensitivity to visual stimuli over learning

Deep learning theory has identified key properties of GD dynamics such as:

1. Learning plateaus, in deep but not shallow networks
2. Local learning, with connected & systematic trajectories
3. A hierarchy of learning stages of increasing complexity

Does animal learning share these properties?