Summary
It is challenging for isolated animals to reliably extract and integrate behaviorally relevant information from the natural environment. Due to the limited sensory capacity of individuals, many animal species therefore share and evaluate cues collectively, allowing them to solve complex decision-making tasks as a group. The behavioral algorithms and neural mechanisms that give rise to these cognitive abilities remain poorly understood. In many fish species, these behaviors are largely vision-based, providing the opportunity to decipher the underlying general computational principles under well-controlled experimental conditions in the lab. At the same time, it is becoming possible to employ powerful neuroscientific techniques, enabling new detailed analyses of the neural circuitry that orchestrates behavior. I propose to establish the juvenile zebrafish as a model system that is optimally suited for the study of collective decision-making. At this intermediate developmental stage, zebrafish offer an excellent compromise between cognitive ability and experimental accessibility. They can temporally and spatially integrate information, they start to socially interact, and one can characterize and manipulate brain activity in intact behaving animals. Using closed-loop virtual reality experiments, I will initially dissect the algorithmic rules by which juvenile zebrafish make decisions when swimming in heterogeneously biased groups. I will then characterize brain activity related to this behavior, in freely swimming fish, and in restrained preparations. Finally, to causally link neural circuit function and group decision-making performance, I will carry out targeted laser ablation and optogenetic activation experiments. Thus, my proposed research in juvenile zebrafish will, for the first time, provide key insights into the behavioral algorithms and neural mechanisms of how individual animals and animal collectives acquire sensory information and make complex decisions.
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| Web resources: | https://cordis.europa.eu/project/id/101075541 |
| Start date: | 01-08-2023 |
| End date: | 31-07-2028 |
| Total budget - Public funding: | 1 498 787,00 Euro - 1 498 787,00 Euro |
Cordis data
Original description
It is challenging for isolated animals to reliably extract and integrate behaviorally relevant information from the natural environment. Due to the limited sensory capacity of individuals, many animal species therefore share and evaluate cues collectively, allowing them to solve complex decision-making tasks as a group. The behavioral algorithms and neural mechanisms that give rise to these cognitive abilities remain poorly understood. In many fish species, these behaviors are largely vision-based, providing the opportunity to decipher the underlying general computational principles under well-controlled experimental conditions in the lab. At the same time, it is becoming possible to employ powerful neuroscientific techniques, enabling new detailed analyses of the neural circuitry that orchestrates behavior. I propose to establish the juvenile zebrafish as a model system that is optimally suited for the study of collective decision-making. At this intermediate developmental stage, zebrafish offer an excellent compromise between cognitive ability and experimental accessibility. They can temporally and spatially integrate information, they start to socially interact, and one can characterize and manipulate brain activity in intact behaving animals. Using closed-loop virtual reality experiments, I will initially dissect the algorithmic rules by which juvenile zebrafish make decisions when swimming in heterogeneously biased groups. I will then characterize brain activity related to this behavior, in freely swimming fish, and in restrained preparations. Finally, to causally link neural circuit function and group decision-making performance, I will carry out targeted laser ablation and optogenetic activation experiments. Thus, my proposed research in juvenile zebrafish will, for the first time, provide key insights into the behavioral algorithms and neural mechanisms of how individual animals and animal collectives acquire sensory information and make complex decisions.Status
SIGNEDCall topic
ERC-2022-STGUpdate Date
09-02-2023
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