Achard, P., & De Schutter, E. (2006). Complex parameter landscape for a complex neuron model. PLoS computational biology, 2, e94.
Article PubMed PubMed Central Google Scholar
Alexandre, F., Dominey, P. F., Gaussier, P., Girard, B., Khamassi, M., & Rougier, N. P. (2020). When artificial intelligence and computational neuroscience meet. In A Guided Tour of Artificial Intelligence Research (pp. 303–335). publisher Springer.
Alonso, L. M., & Marder, E. (2019). Visualization of currents in neural models with similar behavior and different conductance densities. Elife, 8, e42722.
Article PubMed PubMed Central Google Scholar
Alonso, L. M., & Marder, E. (2020). Temperature compensation in a small rhythmic circuit. Elife, 9, e55470.
Article PubMed PubMed Central Google Scholar
Chalasani, S. H., Chronis, N., Tsunozaki, M., Gray, J. M., Ramot, D., Goodman, M. B., & Bargmann, C. I. (2007). Dissecting a circuit for olfactory behaviour in caenorhabditis elegans. Nature, 450, 63–70.
Article CAS PubMed Google Scholar
Deistler, M., Macke, J. H., & Gonçalves, P. J. (2022). Energy-efficient network activity from disparate circuit parameters. Proceedings of the National Academy of Sciences, 119, e2207632119.
Destexhe, A., & Rudolph-Lilith, M. (2012). Neuronal noise volume 8. publisher Springer Science & Business Media.
Druckmann, S., Berger, T. K., Schürmann, F., Hill, S., Markram, H., & Segev, I. (2011). Effective stimuli for constructing reliable neuron models. PLoS Comput Biol, 7, e1002133.
Article CAS PubMed PubMed Central Google Scholar
Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, 98, 13763–13768.
Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature reviews neuroscience, 9, 292–303.
Article CAS PubMed PubMed Central Google Scholar
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. publisher. Cambridge University Press.
Gerstner, W., & Naud, R. (2009). How good are neuron models? Science, 326, 379–380.
Article CAS PubMed Google Scholar
Goaillard, J.-M., & Marder, E. (2021). Ion channel degeneracy, variability, and covariation in neuron and circuit resilience. Annual Review of Neuroscience, 44.
Golowasch, J., Goldman, M. S., Abbott, L., & Marder, E. (2002). Failure of averaging in the construction of a conductance-based neuron model. Journal of neurophysiology, 87, 1129–1131.
Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., et al. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. Elife, 9, e56261.
Article PubMed PubMed Central Google Scholar
Gouwens, N. W., Berg, J., Feng, D., Sorensen, S. A., Zeng, H., Hawrylycz, M. J., Koch, C., & Arkhipov, A. (2018). Systematic generation of biophysically detailed models for diverse cortical neuron types. Nature communications, 9, 1–13.
Grothendieck, A. (2022). Récoltes et semailles. publisher Gallimard.
Hasenstaub, A., Otte, S., Callaway, E., & Sejnowski, T. J. (2010). Metabolic cost as a unifying principle governing neuronal biophysics. Proceedings of the National Academy of Sciences, 107, 12329–12334.
Hobert, O. (2018). Neurogenesis in the nematode caenorhabditis elegans. WormBook: The Online Review of C. elegans Biology [Internet].
Iavarone, E., Yi, J., Shi, Y., Zandt, B.-J., O’reilly, C., Van Geit, W., Rössert, C., Markram, H., & Hill, S. L. (2019). Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLOS Computational Biology, 15, e1006753.
Izhikevich, E. M. (2000). Neural excitability, spiking and bursting. International journal of bifurcation and chaos, 10, 1171–1266.
Izhikevich, E. M. (2007). Dynamical systems in neuroscience. publisherMIT press.
Kamaleddin, M. A. (2021). Degeneracy in the nervous system: from neuronal excitability to neural coding. BioEssays, (p. 2100148).
Le Cun, Y. (2019). Quand la machine apprend: la révolution des neurones artificiels et de l’apprentissage profond. publisherOdile Jacob.
Liu, Q., Kidd, P. B., Dobosiewicz, M., & Bargmann, C. I. (2018). C. elegans awa olfactory neurons fire calcium-mediated all-or-none action potentials. Cell, 175, 57–70.
Article CAS PubMed Google Scholar
Macpherson, T., Churchland, A., Sejnowski, T., DiCarlo, J., Kamitani, Y., Takahashi, H., & Hikida, T. (2021). Natural and artificial intelligence: A brief introduction to the interplay between ai and neuroscience research. Neural Networks, 144, 603–613.
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163, 456–492.
Article CAS PubMed Google Scholar
Mason, P. H., Winter, B., Grignolio, A., et al. (2015). Hidden in plain view: degeneracy in complex systems. Biosystems, 128, 1–8.
Article CAS PubMed Google Scholar
Migliore, R., Lupascu, C. A., Bologna, L. L., Romani, A., Courcol, J.-D., Antonel, S., Van Geit, W. A., Thomson, A. M., Mercer, A., Lange, S., et al. (2018). The physiological variability of channel density in hippocampal ca1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS computational biology, 14, e1006423.
Article PubMed PubMed Central Google Scholar
Naudin, L., Corson, N., & Aziz-Alaoui, M. (2021). A generic conductance-based model of non-spiking caenorhabditis elegans neurons and its mathematical analysis. hal-03494379, .
Naudin, L., Corson, N., Aziz-Alaoui, M., Laredo, J. L. J., & Démare, T. (2020). On the modeling of the three types of non-spiking neurons of the caenorhabditis elegans. International Journal of Neural Systems, (p. S012906572050063X).
Naudin, L., Jiménez Laredo, J. L., Liu, Q., & Corson, N. (2022). Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons. PLoS One, 17, 1–22.
Onasch, S., & Gjorgjieva, J. (2020). Circuit stability to perturbations reveals hidden variability in the balance of intrinsic and synaptic conductances. Journal of Neuroscience, 40, 3186–3202.
Article CAS PubMed Google Scholar
Price, C. J., & Friston, K. J. (2002). Degeneracy and cognitive anatomy. Trends in cognitive sciences, 6, 416–421.
Prinz, A. A., Bucher, D., & Marder, E. (2004). Similar network activity from disparate circuit parameters. Nature neuroscience, 7, 1345–1352.
Article CAS PubMed Google Scholar
Schürmann, F., Courcol, J.-D., & Ramaswamy, S. (2022). Computational concepts for reconstructing and simulating brain tissue. In Computational Modelling of the Brain (pp. 237–259). publisher Springer.
Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., Varol, E., Oikonomou, P., Glenwinkel, L., McWhirter, R., et al. (2021). Molecular topography of an entire nervous system. Cell, 184, 4329–4347.
Article CAS PubMed PubMed Central Google Scholar
Tononi, G., Sporns, O., & Edelman, G. M. (1999). Measures of degeneracy and redundancy in biological networks. Proceedings of the National Academy of Sciences, 96, 3257–3262.
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986). The structure of the nervous system of the nematode caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci, 314, 1–340.
Article CAS PubMed Google Scholar
Yang, J., Shakil, H., Ratté, S., & Prescott, S. A. (2022). Minimal requirements for a neuron to co-regulate many properties and the implications for ion channel correlations and robustness. Elife, 11, e72875.
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