Analysis of slow (theta) oscillations as a potential temporal reference frame for information coding in sensory cortices

PLoS Comput Biol. 2012;8(10):e1002717. doi: 10.1371/journal.pcbi.1002717. Epub 2012 Oct 11.


While sensory neurons carry behaviorally relevant information in responses that often extend over hundreds of milliseconds, the key units of neural information likely consist of much shorter and temporally precise spike patterns. The mechanisms and temporal reference frames by which sensory networks partition responses into these shorter units of information remain unknown. One hypothesis holds that slow oscillations provide a network-intrinsic reference to temporally partitioned spike trains without exploiting the millisecond-precise alignment of spikes to sensory stimuli. We tested this hypothesis on neural responses recorded in visual and auditory cortices of macaque monkeys in response to natural stimuli. Comparing different schemes for response partitioning revealed that theta band oscillations provide a temporal reference that permits extracting significantly more information than can be obtained from spike counts, and sometimes almost as much information as obtained by partitioning spike trains using precisely stimulus-locked time bins. We further tested the robustness of these partitioning schemes to temporal uncertainty in the decoding process and to noise in the sensory input. This revealed that partitioning using an oscillatory reference provides greater robustness than partitioning using precisely stimulus-locked time bins. Overall, these results provide a computational proof of concept for the hypothesis that slow rhythmic network activity may serve as internal reference frame for information coding in sensory cortices and they foster the notion that slow oscillations serve as key elements for the computations underlying perception.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials
  • Animals
  • Auditory Cortex / physiology*
  • Computational Biology
  • Macaca mulatta
  • Models, Neurological*
  • Signal Processing, Computer-Assisted
  • Theta Rhythm / physiology*
  • Time Factors
  • Visual Cortex / physiology*

Grants and funding

This work was supported by the Max Planck Society and was part of the Bernstein Center for Computational Neuroscience, Tübingen, funded by the German Federal Ministry of Education and Research (BMBF; FKZ: 01GQ1002). In addition we acknowledge support from the BMI project of IIT and the SI-CODE project of the Future and Emerging Technologies (FET) program within the Seventh Framework for Research of the European Commission, under FET-Open grant number: FP7-284553. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.