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Zusammenfassung:
Understanding how the brain represents, updates, and accommodates uncertainty is a key challenge for computational
neuroscience. Neuromodulators such as acetylcholine (ACh) have been suggested as playing important
roles over multiple timescales, regulating excitability and plasticity to mediate various effects of uncertainty on
inference and learning. While these influences are consistent with ACh’s long-standing association with general
functions of attention and arousal, recent studies using novel techniques to measure and manipulate this system
with increasing exactitude have revealed intriguing patterns of activity at fast timescales. Notably, Sarter and colleagues
used ACh amperometry (Howe et al., 2013, J.Neurosci., 33(20):8742-8752) and optogenetics (Gritton et
al., 2016, PNAS, 113(8):E1089-E1097) as rodents performed a challenging signal detection task; they reported
effects such as a serial dependency over multiple trials as to whether ACh is released, and increased false alarm
rates when optogenetic stimulation is applied during non-signal trials. Inspired by their task and findings, we
construct an abstract detection task, and consider how attentional state might be optimally controlled over each
trial, assuming that more focused attention improves sensory information but incurs costs. We show similarities
between the resulting attentional dynamics and task performance in the model and experimental results.
