TY - JOUR
T1 - Reduced models for binocular rivalry
AU - Laing, Carlo R.
AU - Frewen, Thomas
AU - Kevrekidis, Ioannis G.
N1 - Funding Information:
Acknowledgements The work of C.R.L. was partially supported by the Marsden Fund, administered by The Royal Society of New Zealand. The work of I.G.K. and T.F. was partially supported by the National Science Foundation and DARPA.
PY - 2010/6
Y1 - 2010/6
N2 - Binocular rivalry occurs when two very different images are presented to the two eyes, but a subject perceives only one image at a given time. A number of computational models for binocular rivalry have been proposed; most can be categorised as either "rate" models, containing a small number of variables, or as more biophysically-realistic "spiking neuron" models. However, a principled derivation of a reduced model from a spiking model is lacking. We present two such derivations, one heuristic and a second using recently-developed data-mining techniques to extract a small number of "macroscopic" variables from the results of a spiking neuron model simulation. We also consider bifurcations that can occur as parameters are varied, and the role of noise in such systems. Our methods are applicable to a number of other models of interest.
AB - Binocular rivalry occurs when two very different images are presented to the two eyes, but a subject perceives only one image at a given time. A number of computational models for binocular rivalry have been proposed; most can be categorised as either "rate" models, containing a small number of variables, or as more biophysically-realistic "spiking neuron" models. However, a principled derivation of a reduced model from a spiking model is lacking. We present two such derivations, one heuristic and a second using recently-developed data-mining techniques to extract a small number of "macroscopic" variables from the results of a spiking neuron model simulation. We also consider bifurcations that can occur as parameters are varied, and the role of noise in such systems. Our methods are applicable to a number of other models of interest.
KW - Binocular rivalry
KW - Data-mining
KW - Diffusion map
KW - Macroscopic
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U2 - 10.1007/s10827-010-0227-6
DO - 10.1007/s10827-010-0227-6
M3 - Article
C2 - 20182782
AN - SCOPUS:77953324890
SN - 0929-5313
VL - 28
SP - 459
EP - 476
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 3
ER -