Sequential and efficient neural-population coding of complex task information

Sue Ann Koay, Adam S. Charles, Stephan Y. Thiberge, Carlos D. Brody, David W. Tank

Research output: Contribution to journalArticlepeer-review


Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.

Original languageEnglish (US)
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)


  • complex decision making behavior
  • efficient coding
  • mouse posterior cortex
  • neural population coding
  • neural sequences


Dive into the research topics of 'Sequential and efficient neural-population coding of complex task information'. Together they form a unique fingerprint.

Cite this