Is anyone interested in the RL ↔ neuroscience “spiral”? Thinking of writing a deep dive series

I’ve been thinking a lot about the relationship between reinforcement learning and neuroscience lately, and something about the usual framing doesn’t quite capture it.

People often say the two fields developed in parallel. But historically it feels more like a spiral.

Ideas move from neuroscience into computational models, then back again. Each turn sharpens the other.

I’m considering writing a deep dive series about this, tentatively called “The RL Spiral.” The goal would be to trace how ideas moved back and forth between the two fields over time, and how that process shaped modern reinforcement learning.

Some topics I’m thinking about:

• Thorndike, behaviorism, and the origins of reward learning
• Dopamine as a reward prediction error signal
• Temporal Difference learning and the Sutton–Barto framework
• How neuroscience experiments influenced RL algorithms (and vice versa)
• Actor–critic and basal ganglia parallels
• Exploration vs curiosity in animals and agents
• What modern deep RL and world models might learn from neuroscience

Curious if people here would find something like this interesting.

Also very open to suggestions.
What parts of the RL ↔ neuroscience connection would you most want a deep dive on?

————- Update ————-

Here is the draft of Part 1 of the series, a light introductory piece:

https://www.robonaissance.com/p/the-rl-spiral-part-1-the-reward-trap

Right now the plan is for the series to have around 8 parts. I’ll likely publish 1–2 parts per week over the next few weeks.

Also, thanks a lot for all the great suggestions in the comments. If the series can’t cover everything, I may eventually expand it into a longer project, possibly even a book, so many of your ideas could make their way into that as well.