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?
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).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Ashwin, P., Timme, M. When instability makes sense. Nature 436, 36–37 (2005). https://doi.org/10.1038/436036b
Published:
Issue Date:
DOI: https://doi.org/10.1038/436036b
- Springer Nature Limited
This article is cited by
-
Controllable branching of robust response patterns in nonlinear mechanical resonators
Nature Communications (2023)
-
Brain Performance versus Phase Transitions
Scientific Reports (2015)
-
A neural coding scheme reproducing foraging trajectories
Scientific Reports (2015)
-
Physiology-based modeling of cortical auditory evoked potentials
Biological Cybernetics (2008)
-
Kinetic Models of Brain Activity
Brain Imaging and Behavior (2008)