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mdhumphries authored Aug 13, 2024
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## Highlights

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### Tracking subjects' choice strategies at trial resolution
Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. In our new paper, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution, and show We show it identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents.
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//{%
// include feature.html
// image="images/Paper_Figures/Maggi_eLife2024_figure5.jpg"
// link="https://doi.org/10.7554/eLife.86491"
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### Computing the probability of connection between two neurons
In a new lab paper, we introduce a new Bayesian approach to computing the probability of connection between classes of neuron, its distance dependence, and the strength of evidence for it. We use this approach to synthesis the most complete map of the striatal microcircuit currently available.
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//{%
// include button.html
// link="https://github.com/Humphries-Lab/Bayesian-map-of-striatum-circuitry"
// text="Get code"
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// flip=true
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include button.html
link="https://github.com/Humphries-Lab/Bayesian-map-of-striatum-circuitry"
text="Get code"
icon="fa-solid fa-arrow-right"
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{%
include feature.html
image="images/1__Neuron_with_a_patch_pipette.jpg"
link="https://doi.org/10.1523/JNEUROSCI.1487-21.2021"
//title="A patched neuron"
text=text
%}

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