Methods to assess binocular rivalry with periodic stimuli
Tóm tắt
Binocular rivalry occurs when the two eyes are presented with incompatible stimuli and perception alternates between these two stimuli. This phenomenon has been investigated in two types of experiments: (1) Traditional experiments where the stimulus is fixed, (2) eye-swap experiments in which the stimulus periodically swaps between eyes many times per second (Logothetis et al. in Nature 380(6575):621–624, 1996). In spite of the rapid swapping between eyes, perception can be stable for many seconds with specific stimulus parameter configurations. Wilson introduced a two-stage, hierarchical model to explain both types of experiments (Wilson in Proc. Natl. Acad. Sci. 100(24):14499–14503, 2003). Wilson’s model and other rivalry models have been only studied with bifurcation analysis for fixed inputs and different types of dynamical behavior that can occur with periodically forcing inputs have not been investigated. Here we report (1) a more complete description of the complex dynamics in the unforced Wilson model, (2) a bifurcation analysis with periodic forcing. Previously, bifurcation analysis of the Wilson model with fixed inputs has revealed three main types of dynamical behaviors: Winner-takes-all (WTA), Rivalry oscillations (RIV), Simultaneous activity (SIM). Our results have revealed richer dynamics including mixed-mode oscillations (MMOs) and a period-doubling cascade, which corresponds to low-amplitude WTA (LAWTA) oscillations. On the other hand, studying rivalry models with numerical continuation shows that periodic forcing with high frequency (e.g. 18 Hz, known as flicker) modulates the three main types of behaviors that occur with fixed inputs with forcing frequency (WTA-Mod, RIV-Mod, SIM-Mod). However, dynamical behavior will be different with low frequency periodic forcing (around 1.5 Hz, so-called swap). In addition to WTA-Mod and SIM-Mod, cycle skipping, multi-cycle skipping and chaotic dynamics are found. This research provides a framework for either assessing binocular rivalry models to check consistency with empirical results, or for better understanding neural dynamics and mechanisms necessary to implement a minimal binocular rivalry model.
Tài liệu tham khảo
Logothetis NK, Leopold DA, Sheinberg DL. What is rivalling during binocular rivalry? Nature. 1996;380(6575):621–4.
Wilson HR. Computational evidence for a rivalry hierarchy in vision. Proc Natl Acad Sci. 2003;100(24):14499–503.
Blake R, Logothetis NK. Visual competition. Nat Rev Neurosci. 2002;3(1):13–21.
Wilke M, Logothetis NK, Leopold DA. Generalized flash suppression of salient visual targets. Neuron. 2003;39(6):1043–52.
Logothetis NK, Schall JD. Neuronal correlates of subjective visual perception. Science. 1989;245(4919):761–3.
Leopold DA, Logothetis NK. Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry. Nature. 1996;379(6565):549–53.
Polonsky A, Blake R, Braun J, Heeger DJ. Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry. Nat Neurosci. 2000;3(11):1153–9.
Tong F, Engel SA. Interocular rivalry revealed in the human cortical blind-spot representation. Nature. 2001;411(6834):195–9.
Zhang P, Jamison K, Engel S, He B, He S. Binocular rivalry requires visual attention. Neuron. 2011;71(2):362–9.
Laing CR, Chow CC. A spiking neuron model for binocular rivalry. J Comput Neurosci. 2002;12(1):39–53.
Shpiro A, Curtu R, Rinzel J, Rubin N. Dynamical characteristics common to neuronal competition models. J Neurophysiol. 2007;97(1):462–73.
Curtu R, Shpiro A, Rubin N, Rinzel J. Mechanisms for frequency control in neuronal competition models. SIAM J Appl Dyn Syst. 2008;7(2):609–49.
Lee S-H, Blake R. Rival ideas about binocular rivalry. Vis Res. 1999;39(8):1447–54.
Sengpiel F, Blakemore C, Harrad R. Interocular suppression in the primary visual cortex: a possible neural basis of binocular rivalry. Vis Res. 1995;35(2):179–95.
Sengpiel F, Freeman T, Blakemore C. Interocular suppression in cat striate cortex is not orientation selective. NeuroReport. 1995;6(16):2235–9.
Li B, Peterson MR, Thompson JK, Duong T, Freeman RD. Cross-orientation suppression: monoptic and dichoptic mechanisms are different. J Neurophysiol. 2005;94(2):1645–50.
Levelt WJM. On binocular rivalry. Soesterberg, The Netherlands: Institutefor Perception RVO-TNO; 1965.
Cao R, Braun J, Mattia M. Stochastic accumulation by cortical columns may explain the scalar property of multistable perception. Phys Rev Lett. 2014;113(9):098103.
Lehky SR. An astable multivibrator model of binocular rivalry. Perception. 1988;17(2):215–28.
Blake R. A neural theory of binocular rivalry. Psychol Rev. 1989;96(1):145–67.
Wilson HR. Minimal physiological conditions for binocular rivalry and rivalry memory. Vis Res. 2007;47(21):2741–50.
Salinas E. Background synaptic activity as a switch between dynamical states in a network. Neural Comput. 2003;15(7):1439–75.
Freeman AW. Multistage model for binocular rivalry. J Neurophysiol. 2005;94(6):4412–20.
Brascamp J, Sohn H, Lee S-H, Blake R. A monocular contribution to stimulus rivalry. Proc Natl Acad Sci. 2013;110(21):8337–44.
Li H-H, Rankin J, Rinzel J, Carrasco M, Heeger DJ. Attention model of binocular rivalry. Proc Natl Acad Sci. 2017;114(30):6192–201.
van Boxtel JJ, Knapen T, Erkelens CJ, van Ee R. Removal of monocular interactions equates rivalry behavior for monocular, binocular, and stimulus rivalries. J Vis. 2008;8(15):13.
Denison RN, Silver MA. Distinct contributions of the magnocellular and parvocellular visual streams to perceptual selection. J Cogn Neurosci. 2012;24(1):246–59.
Vattikuti S, Thangaraj P, Xie HW, Gotts SJ, Martin A, Chow CC. Canonical cortical circuit model explains rivalry, intermittent rivalry, and rivalry memory. PLoS Comput Biol. 2016;12(5):1004903.
Doedel EJ, Fairgrieve TF, Sandstede B, Champneys AR, Kuznetsov YA, Wang X. Auto-07p: continuation and bifurcation software for ordinary differential equations (software package). 2007.
Nowacki J, Osinga HM, Tsaneva-Atanasova K. Dynamical systems analysis of spike-adding mechanisms in transient bursts. J Math Neurosci. 2012;2(1):7.
Ermentrout B, Wechselberger M. Canards, clusters, and synchronization in a weakly coupled interneuron model. SIAM J Appl Dyn Syst. 2009;8(1):253–78.
Curtu R, Rubin J. Interaction of canard and singular Hopf mechanisms in a neural model. SIAM J Appl Dyn Syst. 2011;10(4):1443–79.
Curtu R. Singular Hopf bifurcations and mixed-mode oscillations in a two-cell inhibitory neural network. Phys D: Nonlinear Phenom. 2010;239(9):504–14.
Jayasuriya S, Kilpatrick ZP. Effects of time-dependent stimuli in a competitive neural network model of perceptual rivalry. Bull Math Biol. 2012;74(6):1396–426.
Shpiro A, Moreno-Bote R, Rubin N, Rinzel J. Balance between noise and adaptation in competition models of perceptual bistability. J Comput Neurosci. 2009;27(1):37–54.
Seely J, Chow CC. Role of mutual inhibition in binocular rivalry. J Neurophysiol. 2011;106(5):2136–50.
Blake R, Westendorf DH, Overton R. What is suppressed during binocular rivalry? Perception. 1980;9(2):223–31.
Baker DH, Meese TS, Summers RJ. Psychophysical evidence for two routes to suppression before binocular summation of signals in human vision. Neuroscience. 2007;146(1):435–48.
Moradi F, Heeger DJ. Inter-ocular contrast normalization in human visual cortex. J Vis. 2009;9(3):13.
Berglund N, Gentz B. Stochastic dynamic bifurcations and excitability. In: Stochastic methods in neuroscience. 2008. p. 64–93.
Rankin J, Sussman E, Rinzel J. Neuromechanistic model of auditory bistability. PLoS Comput Biol. 2015;11(11):1004555.
Carter O, Konkle T, Wang Q, Hayward V, Moore C. Tactile rivalry demonstrated with an ambiguous apparent-motion quartet. Curr Biol. 2008;18(14):1050–4.