Role of frequency mismatch in neuronal communication through coherence
- 604 Downloads
Neuronal gamma oscillations have been described in local field potentials of different brain regions of multiple species. Gamma oscillations are thought to reflect rhythmic synaptic activity organized by inhibitory interneurons. While several aspects of gamma rhythmogenesis are relatively well understood, we have much less solid evidence about how gamma oscillations contribute to information processing in neuronal circuits. One popular hypothesis states that a flexible routing of information between distant populations occurs via the control of the phase or coherence between their respective oscillations. Here, we investigate how a mismatch between the frequencies of gamma oscillations from two populations affects their interaction. In particular, we explore a biophysical model of the reciprocal interaction between two cortical areas displaying gamma oscillations at different frequencies, and quantify their phase coherence and communication efficiency. We observed that a moderate excitatory coupling between the two areas leads to a decrease in their frequency detuning, up to ∼6 Hz, with no frequency locking arising between the gamma peaks. Importantly, for similar gamma peak frequencies a zero phase difference emerges for both LFP and MUA despite small axonal delays. For increasing frequency detunings we found a significant decrease in the phase coherence (at non-zero phase lag) between the MUAs but not the LFPs of the two areas. Such difference between LFPs and MUAs behavior is due to the misalignment between the arrival of afferent synaptic currents and the local excitability windows. To test the efficiency of communication we evaluated the success of transferring rate-modulations between the two areas. Our results indicate that once two populations lock their peak frequencies, an optimal phase relation for communication appears. However, the sensitivity of locking to frequency mismatch suggests that only a precise and active control of gamma frequency could enable the selection of communication channels and their directionality.
KeywordsGamma neuronal oscillations Frequency detuning Communication through coherence
R.V. thanks Luiz Lana, Jaan Aru, and David Eriksson for fruitful discussions. The authors also thank Pascal Fries for early discussions on the problem of frequency detuning for CTC. This work has been financially supported by the Ministerio de Ciencia e Innovación (project FIS2012-37655). J.G.O. also acknowledges financial support from the ICREA Academia program. R.V. acknowledges financial support from the Hertie Foundation and Estonian Research Council (project BioMedIT SMTAT13061T).
Conflict of interests
The authors declare that they have no conflict of interest.
- Berens, P., Logothetis, N.K., Tolias, A.S. (2012). Visual population codes - towards a common multivariate framework for cell recording and functional imaging. In Local field potentials, BOLD and spiking activity: relationships and physiological mechanisms (pp. 599–624). MIT Press.Google Scholar
- Bosman, C.A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A.M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., Fries, P. (2012). Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron, 75.5, 875–888. doi: 10.1016/j.neuron.2012.06.037.CrossRefGoogle Scholar
- Buehlmann, A., & Deco, G. (2010). Optimal information transfer in the cortex through synchronization. PLoS Computational Biology, 6(9). doi: 10.1371/journal.pcbi.1000934.
- Eriksson, D., Vicente, R., Schmidt, K. (2011). A linear model of phase dependent power correlations in neuronal oscillations. Frontiers in Computational Neuroscience, 5(34). doi: 10.3389/fncom.2011.00034.
- Kang, Y., Kaneko, T., Ohishi, H., Endo, K., Araki, T. (1994). Spatiotemporally differential inhibition of pyramidal cells in the cat motor cortex. Journal of Neurophysiology, 71.1, 280–293.Google Scholar
- Mountcastle, V.B. (1998). Perceptual neuroscience: the cerebral cortex. Cambridge: Harvard University Press.Google Scholar
- Pikovsky, A., Rosenblum, M., Kurths, J. (2001). Synchronization. A universal concept in nonlinear sciences. Cambridge University Press.Google Scholar
- Roepstorff, A., & Lambert, J.D. (1994). Factors contributing to the decay of the stimulus-evoked IPSC in rat hippocampal CA1 neurons. Journal of Neurophysiology, 72.6, 2911–2926.Google Scholar
- Shadlen, M.N., & Movshon, J.A. (1999). Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron, 24.1(67–77), 111–125.Google Scholar
- Singer, W. (1999). Neuronal synchrony: a versatile code for the definition of relations? Neuron, 24.1(49–65), 111–125.Google Scholar
- Tallon-Baudry, C., Bertrand, O., Delpuech, C., Permier, J. (1997). Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans. Journal of Neuroscience, 17.2, 722–734.Google Scholar
- Thomson, D.J. (1982). Spectrum estimation and harmonic analysis. In Proceedings of the IEEE (Vol. 70.9, pp. 1055–1096). doi: 10.1109/PROC.1982.12433, http://ieeexplore.ieee.org/xpls/abs∖_all.jsp?arnumber=1456701.
- Vicente, R., Gollo, L.L., Mirasso, C.R., Fischer, I., Pipa, G. (2008). Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays. Proceedings of the National Academy of Sciences of the United States of America, 105.44, 17157–17162. doi: 10.1073/pnas.0809353105.