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}
}
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References

SHOWING 1-10 OF 96 REFERENCES

Expected and Unexpected Uncertainty: ACh and NE in the Neocortex

It is suggested that norepinephrine reports the radical divergence of bottom-up inputs from prevailing top-down interpretations, to influence inference and plasticity.

Acetylcholine in cortical inference

Bayesian integration in sensorimotor learning

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.

Uncertainty and Learning

The links between learning and uncertainty are reviewed from three perspectives: statistical theories such as the Kalman filter, psychological models in which differential attention is paid to stimuli with an effect on the speed of learning associated with those stimuli, and neurobiological data on the influence of the neuromodulators acetylcholine and norepinephrine on learning and inference.

Metalearning and neuromodulation

  • K. Doya
  • Psychology, Biology
    Neural Networks
  • 2002

Learning by neurones: role of attention, reinforcement and behaviour.

  • S. Sara
  • Biology, Psychology
    Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie
  • 1998

Inference, Attention, and Decision in a Bayesian Neural Architecture

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.

Inference and computation with population codes.

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.

Neuromodulation: acetylcholine and memory consolidation

  • M. Hasselmo
  • Biology, Psychology
    Trends in Cognitive Sciences
  • 1999

Locus coeruleus-evoked responses in behaving rats: A clue to the role of noradrenaline in memory

...