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Abstract In dynamic environments, animals must closely monitor the effects of their actions to inform switches in behavioral strategy. Anterior cingulate cortex (ACC) neurons track decision outcomes in these environments. Yet, it remains unclear whether ACC neurons similarly monitor behavioral history in static environments and, if so, whether these signals are distinct from movement representations. We recorded large-scale ACC activity in freely moving mice making visual evidence-accumulation decisions. Many ACC neurons exhibited nonlinear mixed selectivity for previous choices and outcomes (trial history) and were modulated by movements. Trial history could be stably decoded from population activity and accounted for a separable component of neural activity than posture and movements. Trial history encoding was conserved across different subjects and was unaffected by fluctuating behavioral biases. These findings demonstrate that trial history monitoring in ACC is implemented in a conserved population code that is independent of the volatility of subjects’ task environment.
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Neural Correlations Across Mice During Spontaneous and Task-Related Behaviors
While the neural commonalities as subjects perform similar task-related behaviors has been previously examined, it is very difficult to ascertain the neural commonalities for spontaneous, task-unrelated behaviors such as grooming. As our ability to record high-dimensional naturalistic behavioral and corresponding neural data increases, we can now try to understand the relationship between different subjects performing spontaneous behaviors that occur rarely in time. Here, we first apply novel machine learning techniques to behavioral video data from four head-fixed mice as they perform a self-initiated decision-making task while their neural activity is recorded using widefield calcium imaging. Across mice, we automatically identify spontaneous behaviors such as grooming and task-related behaviors such as lever pulls. Next, we explore the commonalities between the neural activity of different mice as they perform these tasks by transforming the neural activity into a common subspace, using Multidimensional Canonical Correlation Analysis (MCCA). Finally, we compare the commonalities across different trials in the same subject to those across subjects for different types of behaviors, and find that many recorded brain regions display high levels of correlation for spontaneous behaviors such as grooming. The combined behavioral and neural analysis methods in this paper provide an understanding of how similarly different animals perform innate behaviors.
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- Award ID(s):
- 2219876
- PAR ID:
- 10466594
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 1948-3554
- ISBN:
- 978-1-6654-6292-1
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Baltimore, MD, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NER52421.2023.10123873
