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• 1. Pournaki, A., Merfort, L., Ruiz, J., Kouvaris, N. E., Hövel, P., & Hizanidis, J., Synchronization patterns in modular neuronal networks: A case study of C. elegans. Frontiers in Applied Mathematics and Statistics, 2019.
• 2. Zhou, Y., Qiu, L., Wang, H., & Chen, X., Induction of activity synchronization among primed hippocampal neurons out of random dynamics is key for trace memory formation and retrieval. The FASEB Journal, 2019. 34(3): p. 3658-3676.
• 3. Nikitin, D., Omelchenko, I., Zakharova, A., Avetyan, M., Fradkov, A. L., & Schöll, E., Complex partial synchronization patterns in networks of delay-coupled neurons. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2019. 377(2153) 20180128, p. 1-19.
• 4. Gray, C. M., König, P., Engel, A. K., & Singer, W., Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 1989. 338(6213): p. 334-337.
• 5. Leiberg, S., Lutzenberger, W., & Kaiser, J.,. Effects of memory load on cortical oscillatory activity during auditory pattern working memory. Brain Research, 2006. 1120(1): p. 131-140.
• 6. Fernando, C., & Sojakka, S., Pattern recognition in a bucket. Advances in Artificial Life, ECAL 2003. p. 588-597.
• 7. Timofeev, I., Bazhenov, M., Seigneur, J., & Sejnowski, T., Neuronal synchronization and Thalamocortical rhythms during sleep, wake, and epilepsy. Jasper's Basic Mechanisms of the Epilepsies, 2012 p. 157-175.
• 8. Timme, N. M., & Lapish, C., A tutorial for information theory in neuroscience. Eneuro. ENEURO, 2018. 5(3), https://doi.org/10.1523/eneuro.0052-18. 2018.
• 9. Gençağa, D., Şengül Ayan, S., Farnoudkia, H., & Okuyucu, S., Statistical approaches for the analysis of dependency among neurons under noise. Entropy, 2020. 22(4): 387.
• 10. Jæger, K. H., Wall, S., & Tveito, A., Detecting undetectables: Can conductances of action potential models be changed without appreciable change in the transmembrane potential?. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019. 29(7): 073102.
• 11. Liu, Z., Golowasch, J., Marder, E., & Abbott, L. F., A model Neuron with activity-dependent conductances regulated by multiple calcium sensors. The Journal of Neuroscience, 1998. 18(7): p. 309-2320.
• 12. Fernandez, F. R., & White, J. A., Reduction of spike Afterdepolarization by increased leak conductance alters Interspike interval variability. Journal of Neuroscience, 2009. 29(4): p. 973-986.
• 13. Şengül Ayan, S., Sırcan, A. K., Abewa, M., Kurt, A., Dalaman, U., & Yaraş, N., Mathematical model of the ventricular action potential and effects of isoproterenol-induced cardiac hypertrophy in rats. European Biophysics Journal, 2020. 49(5): p. 323-342.
• 14. Duncan, P. J., Sengul, S., Tabak, J., Ruth, P., Bertram, R., & Shipston, M. J., Large conductance ca2+-activated K+channels (BK) promote secretagogue-induced transition from spiking to bursting in murine anterior pituitary corticotrophs. The Journal of Physiology, 2014. 593(5): p. 1197-211.
• 15. Patel, A. X., & Burdakov, D., Mechanisms of gain control by voltage-gated channels in intrinsically-firing neurons. Plos One, 2015. 10(3), e0115431.
• 16. Gençağa, D., & Ayan, S. Ş., Effects of neuronal noise on neural communication. Proceedings of The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2019. 33(1): p. 2.
• 17. Lane, B. J., Samarth, P., Ransdell, J. L., Nair, S. S., & Schulz, D. J., Synergistic plasticity of intrinsic conductance and electrical coupling restores synchrony in an intact motor network. eLife, 2016. 5. https://doi.org/10.7554/elife.16879.
• 18. Cover, T. M., Thomas, J. A., Information theory and portfolio theory. Elements of Information Theory, 2005. p:613-656. USA: John Wiley & Sons, Inc.
• 19. Schreiber, T., Measuring information transfer. Physical Review Letters, 2000. 85(2): p. 461-464.
• 20. Scott, D. W., Multivariate density estimation. 2012, USA: Wiley Series in Probability and Statistics.
• 21. Gençağa, D., Transfer entropy. Entropy, 2018. 20(4): p. 288. https://doi.org/10.3390/e20040288.
• 22. Hodgkin, A. L., & Huxley, A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 1952. 117(4): p.500-544.
• 23. Dhanya E, Pradhan, N., Sunitha R, & Sreedevi, A., Modelling and implementation of two coupled Hodgkin-Huxley Neuron model. 2015 International Conference on Computing and Network Communications (CoCoNet).
• 24. Ermentrout, B., Simulating, analyzing, and animating dynamical systems. 2002, USA: Society for Industrial and Applied Mathematics.
• 25. Şengül, S., Clewley, R., Bertram, R., & Tabak, J., Determining the contributions of divisive and subtractive feedback in the Hodgkin-Huxley model. Journal of Computational Neuroscience, 2014. 37(3): p. 403-415.
• 26. Bezanilla, F., Rojas, E., & Taylor, R. E., Sodium and potassium conductance changes during a membrane action potential. The Journal of Physiology, 1970. 211(3): p. 729-751.