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Mark Burrell

@mhburrell.bsky.social
42 followers 28 following 12 posts
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Reposted by Mark Burrell
Malcolm Campbell @malcolmgcampbell.bsky.social · 19/09/2025
🚨Our preprint is online!🚨 www.biorxiv.org/content/10.1... How do #dopamine neurons perform the key calculations in reinforcement #learning? Read on to find out more! 🧵
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
Read more about it and see our smiling faces: www.mcb.harvard.edu/department/news… @harvardmcb.bsky.social (12/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
This work is a product of a tremendous team: Lechen (Selina) Qian, Jay A Hennig (@jhennig.bsky.social), Sara Matias (@saramatias.bsky.social), Venki Murthy (@neurovenki.bsky.social), Sam Gershman (@gershbrain.bsky.social) and Naoshige Uchida (@naoshigeuchida.bsky.social) (11/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
We thank the reviewers for their comments, who helped us refine our explanations of the various models and why they succeed or fail. (10/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
In total, we show how TD learning can be used as a comprehensive explanation of the effects of contingency on associative learning and discuss how this guides our future study of how the brain learns causality. (9/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
Finally, we showed a novel model that relies on retrospective contingency, ANCCR (doi:10.1126/science.abq6740), does not explain our results under any parameter combination. (8/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
Moreover, working with @jhennig.bsky.social, we showed small RNNS develop similar state space representations and explain our results in the same manner. (7/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
We sought to identify a TD model to explain these changes. While several classic implementations of TD did not working (e.g. CSC & microstimuli), with a state representation that incorporated the animal’s learned knowledge of the task structure, TD was able to explain all our results. (6/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
Another group, received the same increase in the number of rewards, but new rewards were preceded by a novel cue. This important control reveals that a classic definition of contingency ∆𝑃 does not adequately describe the pattern of changes in dopamine and behavior. (5/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
We performed a Pavlovian contingency degradation task to examine how behavior and dopamine activities are modulated in contingency learning. In this task, mice were first trained in a simple conditioning task. Then one group of mice received both cued and uncued rewards. (4/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
Contingency, the degree to which a stimulus predicts an outcome, is a critical factor in shaping animal behavior during associative learning. But the neural mechanisms linking contingency to behavior have been elusive. (3/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
In short, we found that we can explain the effects of contingency on both an animal’s behavior and ventral striatum dopamine response using temporal difference learning when equipped with appropriate state space representations. (2/12)
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Mark Burrell @mhburrell.bsky.social · 18/03/2025
I’m happy to share our latest work (co-lead by Selina Qian) has today been published in its final form in @:natureneuro.bsky.social: Read here: www.nature.com/articles/s41593-025-… (1/12)
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