Literature DB >> 15896570

Information theory, novelty and hippocampal responses: unpredicted or unpredictable?

Bryan A Strange1, Andrew Duggins, William Penny, Raymond J Dolan, Karl J Friston.   

Abstract

Shannon's information theory provides a principled framework for the quantitative analysis of brain responses during the encoding and representation of event streams. In particular, entropy measures the expected uncertainty of events in a given context. This contextual uncertainty or unpredictability may, itself, be important for balancing [bottom-up] sensory information and [top-down] prior expectations during perceptual synthesis. Using event-related functional magnetic resonance imaging (fMRI), we found that the anterior hippocampus is sensitive to the entropy of a visual stimulus stream. In contrast, activity in an extensive bilateral cortico-thalamic network was dictated by the surprise or information associated with each particular stimulus. In short, we show that the probabilistic structure or context in which events occur is an important predictor of hippocampal activity.

Mesh:

Year:  2005        PMID: 15896570     DOI: 10.1016/j.neunet.2004.12.004

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  90 in total

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Authors:  Birte U Forstmann; Scott Brown; Gilles Dutilh; Jane Neumann; Eric-Jan Wagenmakers
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8.  Integrated Bayesian models of learning and decision making for saccadic eye movements.

Authors:  Kay H Brodersen; Will D Penny; Lee M Harrison; Jean Daunizeau; Christian C Ruff; Emrah Duzel; Karl J Friston; Klaas E Stephan
Journal:  Neural Netw       Date:  2008-09-07

9.  Adaptive and aberrant reward prediction signals in the human brain.

Authors:  Jonathan P Roiser; Klaas E Stephan; Hanneke E M den Ouden; Karl J Friston; Eileen M Joyce
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10.  Bayesian model selection maps for group studies.

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