Continuous behavioral dynamics in mice under self-paced and socially embedded conditions in the IntelliCage

Abstract

Naturalistic neuroscience seeks to understand behavior in settings where perception, decision-making, and action are continuously coupled. In such closed-loop environments, animals actively determine which information they sample and how they act, giving rise to temporally extended behavioral policies rather than discrete trial-based responses.

Here, we use the IntelliCage system as a semi-naturalistic framework to study continuous behavior in group-housed mice. Animals live in social groups and freely interact with an automated environment in a self-paced, reward-driven manner, enabling the observation of ongoing action selection within a stable social context. Within this framework, a broad range of established behavioral paradigms can be implemented without imposing trial structure. As examples, we present spatial learning experiments probing reward-based memory[1,2], as well as stress paradigms assessing depression-like phenotypes via changes in hedonic behavior[3,4,5]. These paradigms allow us to quantify how internal states shape behavioral policies over extended timescales, reflected in systematic changes in exploration, activity patterns, and reward-related behavior. The continuous nature of the data further enables the analysis of behavior as trajectories through state and action space, rather than isolated responses.

Compared to classical assays, this framework enables high-throughput, long-term recording of multiple animals in parallel with minimal experimenter intervention. This provides a scalable platform for linking experimentally controlled manipulations to adaptive behavior under conditions that approximate natural environments and support quantitative analysis of decision-making and learning.

References

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[3] Crux, Musacchio, and Nebeling et al., in preparation
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