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SUMMARY:Dopamine encodes deep network teaching signals for individual lear
 ning trajectories - Samuel Liebana (UCL)
DTSTART;VALUE=DATE-TIME:20250904T150000
DTEND;VALUE=DATE-TIME:20250904T160000
UID:https://talks.ox.ac.uk/talks/id/7c589e8b-fda0-45d3-8290-8b2ec0cf404d/
DESCRIPTION:Striatal dopamine plays fundamental roles in fine-tuning learn
 ed decisions. However\, when learning from naive to expert\, individuals o
 ften exhibit diverse learning trajectories\, defying understanding of its 
 underlying dopaminergic mechanisms. Here\, we longitudinally measure and m
 anipulate dorsal striatal dopamine signals in mice learning a decision tas
 k from naive to expert. Mice learning trajectories transitioned through se
 quences of strategies\, showing substantial individual diversity. Remarkab
 ly\, the transitions were systematic\; each mouse’s early strategy deter
 mined its strategy weeks later. Dopamine signals reflected strategies each
  animal transitioned through\, encoding a subset of stimulus-choice associ
 ations. Optogenetic manipulations selectively updated these associations\,
  leading to learning effects distinct from that of reward. A deep neural n
 etwork using heterogeneous teaching signals\, each updating a subset of ne
 twork association weights\, captured our results. Analyzing the model’s 
 fixed points explained learning diversity and systematicity. Altogether\, 
 this work provides insights into the biological and mathematical principle
 s underlying individual long-term learning trajectories.\nSpeakers:\nSamue
 l Liebana (UCL)
LOCATION:Sherrington Library\, off Parks Road OX1 3PT
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/7c589e8b-fda0-45d3-8290-8b2ec0cf404d/
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DESCRIPTION:Talk:Dopamine encodes deep network teaching signals for indivi
 dual learning trajectories - Samuel Liebana (UCL)
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