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Reply to Vinken and Vogels.

Andrew H Bell1, Chris Summerfield2, Elyse L Morin3

  • 1Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda MD 20892, USA; Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK.

Current Biology : CB
|November 22, 2017
PubMed
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Information-theoretic (IT) neurons encode long-term, latent probabilistic information about stimulus occurrence. Alternative models based on neuronal fatigue do not fully explain these findings without latent variables.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The study investigates the encoding of probabilistic information by Information-theoretic (IT) neurons.
  • A previous claim stated that IT neurons encode long-term, latent probabilistic information regarding stimulus occurrence.

Discussion:

  • Vinken and Vogels proposed a biologically plausible model based on neuronal fatigue to explain the findings.
  • This alternative model, however, also relies on slowly modulated latent variables, similar to the original account.
  • Models solely based on temporally local fatigue effects cannot account for the observed data.

Key Insights:

  • IT neurons encode latent probabilistic information about stimulus occurrence.
  • Neuronal fatigue models require latent variables to explain the findings, mirroring the original hypothesis.

Related Experiment Videos

  • The proposed critique largely restates the original findings using different terminology and a more complex model.

    • Outlook:

    • Further research is needed to elucidate the precise mechanisms of visual expectation.
    • Clarifying the role of latent variables in neuronal processing remains a key challenge.
    • Developing more parsimonious models that explain neuronal encoding of probabilistic information is essential.