Regulation and prediction of multistable perception alternation

Chen, Ruyin; Xiong, Yue; Zhuge, Shengying; Li, Zekun; Chen, Q; He, Zhifen; Wu, Dingqiang; Hou, Fang; Zhou, Jiawei

doi:10.1016/j.chaos.2023.113564

Cited by 2 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The physical processes underpinning the dynamics of switching between the perceptual states of ambiguous figures remains a subject of debate [7,73]. One of the currently accepted theories suggests that the switching is likely to be explained by chaotic processes observed in nonlinear dynamical systems [51,56,[74][75][76]. Indeed, broadly speaking, the brain is a dynamical system that exhibits a complex nonlinear and chaotic behaviour at multiple levels [77][78][79].…”

Section: Deep Neural Network Algorithmmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

View full text Add to dashboard Cite

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

show abstract

Section: Deep Neural Network Algorithmmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…The last term h 1/α i η i (t) in Eqs. ( 9) and (10), which has the same expression as the free Brownian particle by the Lévy noise, plays a very important role in determining the dynamical behaviors of the system. The stable index of the Lévy noise is smaller than 2, the free Brownian particle will make a superdiffusive motion, while it will make a normal diffusive motion for α i = 2.…”

Section: Resultsmentioning

confidence: 99%

“…A symmetric BM based on the Feynman ratchet has been proposed, [7,8] which performs like a Carnot engine. Although Gaussian white noise is involved in many aspects, such as binocular rivalry, [9][10][11] transport of coupled BM, [12] simulated molecular dynamics, [13] stochastic gene transcriptional dynamics [14] and so on, BMs work with small current and low energy conversion efficiency due to Gaussian white noise.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Brownian motor subjected to Lévy noise

Jia

Nie

2024

Chinese Phys. B

View full text Add to dashboard Cite

In the past years, one mainly focused on the symmetric Brownian motor (BM) with Gaussian noises, whose current and energy conversion efficiency are very low. Here, we investigate operating performance of the symmetric BM subjected to Lévy noise. Through numerical simulations, it is found that operating performance of the motor can be greatly improved in the asymmetric Lévy noise. Without any load, the Lévy noises with smaller stable indexes can let the motor give rise to a much greater current. With load, energy conversion efficiency of the motor can be enhanced more pronouncedly by adjusting the stable indexes of the Lévy noises with symmetry breaking. The research results are of great significance for opening up BM’s intrinsic physical mechanism and promoting development of nanotechnology.

show abstract

Regulation and prediction of multistable perception alternation

Cited by 2 publications

References 65 publications

Quantum-Inspired Neural Network Model of Optical Illusions

Quantum-Inspired Neural Network Model of Optical Illusions

Symmetric Brownian motor subjected to Lévy noise

Contact Info

Product

Resources

About