Nonlinear dynamics in a complex cubic one-dimensional model for sensory psychophysics

DOI10.1007/BF00047112zbMath0629.92026OpenAlexW2090258483MaRDI QIDQ1094359

Robert A. M. Gregson, Ian R. Price

Publication date: 1988

Published in: Acta Applicandae Mathematicae (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00047112

zbMATH Keywords

Lyapunov exponentsdeterministic chaosdissipative systemsnonlinear modellingpsychophysicssensory noisecubic recursionfeedforward processeshigher neural networksone-dimensional noninvertible maps of intervalsreal and imaginary trajectoriessensory intensity signal transmission

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Difference equations (39A99) Psychophysics and psychophysiology; perception (91E30)

Related Items

Similarities derived from 3-d nonlinear psychophysics: Variance distributions ⋮ Julia sets for the gamma recursion in nonlinear psychophysics

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The complex Lorenz equations
• The universal metric properties of nonlinear transformations
• Dimensions and entropies in chaotic systems. Quantification of complex behavior. Proceedings of an International Workshop at the Pecos River Ranch, New Mexico, September 11-16, 1985
• Measuring the strangeness of strange attractors
• The dynamics of a discrete population model with threshold
• Persistent states of neural networks and the random nature of synaptic transmission
• Quantitative universality for a class of nonlinear transformations
• On finite limit sets for transformations on the unit interval
• Strange attractors and chaotic motions of dynamical systems
• On Lienard oscillator models with a pregiven set of limit cycles
• Stable Orbits and Bifurcation of Maps of the Interval
• Deterministic Nonperiodic Flow
• Oscillation and Chaos in Physiological Control Systems
• Simple mathematical models with very complicated dynamics