Mathematical models that use instabilities to describe changes of weather patterns or spacecraft trajectories are well established. Could such principles apply to the sense of smell, and to other aspects of neural computation?
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References
Rabinovich, M. et al. Phys. Rev. Lett. 87, 068102 (2001).
Huerta, R. et al. Neural Comput. 16, 1601–1640 (2004).
Huerta, R. & Rabinovich, M. Phys. Rev. Lett. 93, 238104 (2004).
Stewart, I. Nature 422, 571–573 (2003).
Taubes, G. Science 283, 620–622 (1999).
Hansel, D., Mato, G. & Meunier, C. Phys. Rev. E 48, 3470–3477 (1993).
Kori, H. & Kuramoto, Y. Phys. Rev. E 62, 046214 (2001).
Hopfield, J. J. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Laurent, G. Nature Rev. Neurosci. 3, 884–895 (2002).
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Ashwin, P., Timme, M. When instability makes sense. Nature 436, 36–37 (2005). https://doi.org/10.1038/436036b
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DOI: https://doi.org/10.1038/436036b
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