A probabilistic hammer for nailing complex neural data analyses

József Fiser*, Ádám Koblinger*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

In this issue of Neuron, Młynarski et al. (2021) provide a maxent-based normative method for flexible neural data analysis by combining data-driven and theory-driven approaches. The next challenge is identifying the right frameworks to use this method at its best.

Original languageEnglish
Pages (from-to)1077-1079
Number of pages3
JournalNeuron
Volume109
Issue number7
DOIs
StatePublished - 7 Apr 2021

Keywords

  • Bayesian
  • loss calibration
  • neural data analysis
  • optimization

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