@article {69659,
	title = {A global dopaminergic learning rate enables adaptive foraging across many options},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Nov 05},
	abstract = {<p>In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ( 2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.</p>
},
	doi = {10.1101/2024.11.04.621923},
	url = {https://doi.org/10.1101/2024.11.04.621923},
	author = {Grima, Laura L and Guo, Yipei and Narayan, Lakshmi and Hermundstad, Ann M and Dudman, Joshua T}
}
@article {68414,
	title = {High-throughput automated methods for classical and operant conditioning of larvae.},
	journal = {eLife},
	volume = {11},
	year = {2022},
	month = {2022 Oct 28},
	abstract = {<p>Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i. e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i. e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.</p>
},
	issn = {2050-084X},
	doi = {10.7554/eLife.70015},
	author = {Croteau-Chonka, Elise C and Clayton, Michael S and Venkatasubramanian, Lalanti and Harris, Samuel N and Jones, Benjamin M W and Narayan, Lakshmi and Winding, Michael and Masson, Jean-Baptiste and Zlatic, Marta and Klein, Kristina T}
}
