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High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons

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  • Vicente Reyes-Puerta
  • Suam Kim
  • Jyh-Jang Sun
  • Barbara Imbrosci
  • Werner Kilb
  • Heiko J Luhmann

Abstract

The manner in which populations of inhibitory (INH) and excitatory (EXC) neocortical neurons collectively encode stimulus-related information is a fundamental, yet still unresolved question. Here we address this question by simultaneously recording with large-scale multi-electrode arrays (of up to 128 channels) the activity of cell ensembles (of up to 74 neurons) distributed along all layers of 3–4 neighboring cortical columns in the anesthetized adult rat somatosensory barrel cortex in vivo. Using two different whisker stimulus modalities (location and frequency) we show that individual INH neurons – classified as such according to their distinct extracellular spike waveforms – discriminate better between restricted sets of stimuli (≤6 stimulus classes) than EXC neurons in granular and infra-granular layers. We also demonstrate that ensembles of INH cells jointly provide as much information about such stimuli as comparable ensembles containing the ~20% most informative EXC neurons, however presenting less information redundancy – a result which was consistent when applying both theoretical information measurements and linear discriminant analysis classifiers. These results suggest that a consortium of INH neurons dominates the information conveyed to the neocortical network, thereby efficiently processing incoming sensory activity. This conclusion extends our view on the role of the inhibitory system to orchestrate cortical activity.Author Summary: Perception of the environment relies on neuronal computation in the cerebral cortex. However, the exact algorithms by which cortical neuronal networks process relevant information from the inputs of sensory organs are only poorly understood. To address this problem we stimulated distinct whiskers and recorded the neuronal responses from identified cortical whisker representations of the rat using multi-site electrodes. For rodents the whisker system is one main sensory input channel, offering the unique property that for each whisker an identified cortical area ("barrel-related column") represents its main cortical input station. In the present study we were able to demonstrate that the action potential firing of single inhibitory neurons provides more information about behaviorally relevant qualities of whisker stimulation (identity of the stimulated whisker and frequency of stimulation) than excitatory neurons. In addition, information about stimulation qualities was encoded with less redundancy in inhibitory neurons. In summary, the results of our study suggest that inhibitory neurons carry substantial information about the sensory environment and can thereby adequately orchestrate neuronal activity in sensory cortices.

Suggested Citation

  • Vicente Reyes-Puerta & Suam Kim & Jyh-Jang Sun & Barbara Imbrosci & Werner Kilb & Heiko J Luhmann, 2015. "High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-32, June.
  • Handle: RePEc:plo:pcbi00:1004121
    DOI: 10.1371/journal.pcbi.1004121
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    References listed on IDEAS

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    1. Ehud Ahissar & Ronen Sosnik & Sebastian Haidarliu, 2000. "Transformation from temporal to rate coding in a somatosensory thalamocortical pathway," Nature, Nature, vol. 406(6793), pages 302-306, July.
    2. Tom Tetzlaff & Moritz Helias & Gaute T Einevoll & Markus Diesmann, 2012. "Decorrelation of Neural-Network Activity by Inhibitory Feedback," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-29, August.
    3. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    4. Bilal Haider & Michael Häusser & Matteo Carandini, 2013. "Inhibition dominates sensory responses in the awake cortex," Nature, Nature, vol. 493(7430), pages 97-100, January.
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