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Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli

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  • Ovidiu F Jurjuţ
  • Danko Nikolić
  • Wolf Singer
  • Shan Yu
  • Martha N Havenith
  • Raul C Mureşan

Abstract

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5–20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.

Suggested Citation

  • Ovidiu F Jurjuţ & Danko Nikolić & Wolf Singer & Shan Yu & Martha N Havenith & Raul C Mureşan, 2011. "Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-15, February.
  • Handle: RePEc:plo:pone00:0016758
    DOI: 10.1371/journal.pone.0016758
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    References listed on IDEAS

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    1. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    2. Gaëlle Desbordes & Jianzhong Jin & Chong Weng & Nicholas A Lesica & Garrett B Stanley & Jose-Manuel Alonso, 2008. "Timing Precision in Population Coding of Natural Scenes in the Early Visual System," PLOS Biology, Public Library of Science, vol. 6(12), pages 1-11, December.
    3. Daniel A. Butts & Chong Weng & Jianzhong Jin & Chun-I Yeh & Nicholas A. Lesica & Jose-Manuel Alonso & Garrett B. Stanley, 2007. "Temporal precision in the neural code and the timescales of natural vision," Nature, Nature, vol. 449(7158), pages 92-95, September.
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