C
Christopher D. Fiorillo
Researcher at KAIST
Publications - 35
Citations - 5126
Christopher D. Fiorillo is an academic researcher from KAIST. The author has contributed to research in topics: Dopamine & Reward system. The author has an hindex of 18, co-authored 35 publications receiving 4798 citations. Previous affiliations of Christopher D. Fiorillo include Stanford University & University of Cambridge.
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Discrete coding of reward probability and uncertainty by dopamine neurons.
TL;DR: Using distinct stimuli to indicate the probability of reward, it was found that the phasic activation of dopamine neurons varied monotonically across the full range of probabilities, supporting past claims that this response codes the discrepancy between predicted and actual reward.
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Adaptive Coding of Reward Value by Dopamine Neurons
TL;DR: It is found that midbrain dopamine neurons rapidly adapted to the information provided by reward-predicting stimuli and maintained their reward sensitivity over a large range of reward values.
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The temporal precision of reward prediction in dopamine neurons
TL;DR: The neural precision of expectation appeared to be at least qualitatively similar to the precision of anticipatory licking behavior, and activations to reward increased steeply and linearly with the logarithm of the interval.
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Glutamate mediates an inhibitory postsynaptic potential in dopamine neurons
TL;DR: In this paper, the activation of metabotropic glutamate receptors (mGluR1) mobilized calcium from caffeine/ryanodine-sensitive stores and increased an apamin-sensitive potassium conductance.
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Explicit neural signals reflecting reward uncertainty
Wolfram Schultz,Kerstin Preuschoff,Colin F. Camerer,Ming Hsu,Christopher D. Fiorillo,Christopher D. Fiorillo,P. Tobler,Peter Bossaerts +7 more
TL;DR: Behavioural neurophysiological studies on dopamine neurons revealed a risk signal, which covaried with the standard deviation or variance of the magnitude of juice rewards and occurred separately from reward value coding, which fulfilled a requirement for the mean variance approach of economic decision theory.