Uncertainty, Neuromodulation, and Attention

@article{Yu2005UncertaintyNA,
  title={Uncertainty, Neuromodulation, and Attention},
  author={Angela J. Yu and Peter Dayan},
  journal={Neuron},
  year={2005},
  volume={46},
  pages={681-692}
}
Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known… Expand
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It is proposed that the neuromodulatory systems such as acetylcholine (ACh) and norepinephrine (NE) play a major role in the brain's implementation of these uncertainty computations, and they exhibit properties that are consistent with a diverse body of pharmacological, behavioral, electrophysiological, and neurological findings. Expand
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It is postulated that orbitofrontal cortex transforms the stimulus representation through recurrent dynamics, such that a simple error-driven learning rule operating on the transformed representation can implement the Bayesian reinforcement learning update. Expand
Dopamine, Inference, and Uncertainty
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It is postulated that orbitofrontal cortex transforms the stimulus representation through recurrent dynamics, such that a simple error-driven learning rule operating on the transformed representation can implement the Bayesian reinforcement learning update. Expand
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This model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty, and identifies its interactions with cortical inhibitory interneurons and derives a biophysically grounded computational model able to capture and learn from uncertainty. Expand
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TLDR
Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels are reviewed. Expand
The neurobiology of uncertainty: implications for statistical learning
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  • Computer Science, Medicine
  • Philosophical Transactions of the Royal Society B: Biological Sciences
  • 2017
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It may be useful to establish closer links between neurobiological research on uncertainty and SL, considering particularly mechanisms sensitive to local and global structure in inputs, the degree of input uncertainty, the complexity of the system generating the input, learning mechanisms that operate on different temporal scales and the use of learnt information for online prediction. Expand
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References

SHOWING 1-10 OF 121 REFERENCES
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex
TLDR
It is suggested that norepinephrine reports the radical divergence of bottom-up inputs from prevailing top-down interpretations, to influence inference and plasticity. Expand
Acetylcholine in cortical inference
TLDR
A theory of cholinergic modulation in perceptual inference is developed, suggesting that ACh levels reflect the uncertainty associated with top-down information, and have the effect of modulating the interaction between top- down and bottom-up processing in determining the appropriate neural representations for inputs. Expand
Uncertainty and Learning
It is a commonplace in statistics that uncertainty about parameters drives learning. Indeed one of the most influential models of behavioural learning has uncertainty at its heart. However, many… Expand
Bayesian integration in sensorimotor learning
TLDR
This work shows that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. Expand
Metalearning and neuromodulation
  • K. Doya
  • Mathematics, Computer Science
  • Neural Networks
  • 2002
TLDR
A computational theory on the roles of the ascending neuromodulatory systems from the viewpoint that they mediate the global signals that regulate the distributed learning mechanisms in the brain is presented. Expand
Learning by neurones: role of attention, reinforcement and behaviour.
  • S. Sara
  • Psychology, Medicine
  • Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie
  • 1998
TLDR
Evidence is provided for the specific activation of the locus coeruleus noradrenergic system of the rat by novel stimuli encountered while investigating the environment, as well as during a formal learning situation. Expand
Inference, Attention, and Decision in a Bayesian Neural Architecture
TLDR
A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and top-down attentional priors, leading to sound perceptual discrimination and a possible reconciliation of cortical and neuromodulatory representations of uncertainty. Expand
Inference and computation with population codes.
TLDR
This paper reviews both approaches to neural computation, with a particular emphasis on the latter, which the authors see as a very promising framework for future modeling and experimental work. Expand
Neuromodulation: acetylcholine and memory consolidation
  • M. Hasselmo
  • Psychology, Medicine
  • Trends in Cognitive Sciences
  • 1999
TLDR
It is suggested that the high levels of acetylcholine that are present during active waking might set the appropriate dynamics for encoding new information in the hippocampus, by partially suppressing excitatory feedback connections and so facilitating encoding without interference from previously stored information. Expand
Locus coeruleus-evoked responses in behaving rats: A clue to the role of noradrenaline in memory
TLDR
Single units of the locus coeruleus are recorded during controlled behavioral situations, in order to relate LC activation to specific behavioral contexts, and show that LC cells respond to novelty or change in incoming information, but do not have a sustained response to stimuli, even when they have a high level of biological significance. Expand
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