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Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.
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Predicting rare earth elements concentration in coal ashes with multi-task neural networks
Our multi-task neural network approach simultaneously predicts the concentration of all types of rare earth elements (REEs) in coal ashes, with an improved accuracy and robustness as compared to conventional single-task neural networks.
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- Award ID(s):
- 1922167
- PAR ID:
- 10579759
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Materials Horizons
- Volume:
- 11
- Issue:
- 6
- ISSN:
- 2051-6347
- Page Range / eLocation ID:
- 1448 to 1464
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3MH01491F
