Acknowledgments: We thank Guillaume Hennequin, Jean-Claude Béïque and Matthew Larkum for helpful discussions. We thank Loreen Hertäg, Alexandre Payeur and Stephen E. Clarke for critical reading of the manuscript as well as Greg Knoll for an independent verification of the numerical results. This work was supported by a Bernstein Award (01GQ1201) by the German Federal Ministry for Science and Education and an NSERC Discovery Grant 06872. Part of this work was conducted (RN and HS) at the Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, UK.Author Declarations: The authors declare no conflict of interest.Keywords: neural coding |cerebral cortex | bursting | multiplexing | decoding | spike timing | short-term plasticity Author Contributions: HS and RN designed the study and wrote the manuscript. RN performed the simulations analyzed the results and carried out the theoretical analysis. peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/143636 doi: bioRxiv preprint first posted online Jun. 30, 2017;
AbstractMany cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing rate output, which collapses all input streams into one. We propose that neurons can simultaneously represent multiple input streams by using a novel code that distinguishes single spikes and bursts at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. It also suggests specific connectivity patterns that allows to demultiplex this information. These connectivity patterns can be used by the nervous system to maintain optimal multiplexing. Contrary to firing rate coding, our findings indicate that a single neural ensemble can communicate multiple independent signals to different targets.