Yu, Angela Jie (2005)
ACh and NE: Bayes, Uncertainty, Attention, and Learning.
Gatsby Computational Neuroscience Unit, University College London, University of London
Dissertation, Bibliographie

Kurzbeschreibung (Abstract)

Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and learning, based on imperfect observation in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulatory systems such as acetylcholine (ACh) and norepinephrine (NE) play a major role in the brain’s implementation of these uncertainty computations. ACh and NE have long been supposed to be critically involved in cognitive processes such as attention and learning. However, there has been little consensus on their precise computational functions. We propose that acetylcholine reports expected uncertainty; norepinephrine signals unexpected uncertainty. The interaction between these formally distinct sorts of uncertainty is suggested as playing a important role in mediating the interaction between top-down and bottom-up processing in inference and learning.

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