Abstract
Watching an ambiguous image leads to the bistability of its perception, that randomly oscillates between two possible interpretations. The relevant evolution of the neuron system is usually described with the equation of its “movement” over the nonuniform energy landscape under the action of the stochastic force. We utilize the alternative approach suggesting that the system is in the quasi-stationary state being described by the Arrhenius equation. The latter determines the probability of the dynamical variation of the image (for example, the left and right Necker cubes [1]) along one scenario or another. Probabilities of transitions from one perception to another are defined by barriers that detach corresponding wells of the energy landscape, and the relative value of the noise influencing this process. The mean noise value could be estimated from experimental data. The model predicts logarithmic dependence of the perception hysteresis width on the period of cyclic sweeping the parameter, controlling the perception (for instance, the contrast of the presented object). It agrees with the experiment and allows to estimate the time interval between two various perceptions.
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Notes
- 1.
By fluctuations we mean the deviation of ion or neurotransmitter concentrations in synaptic contacts. That is why we call this noise as chemical one. This term is purely phenomenal, different processes could group together under this same heading. But, nevertheless, the electric potential of a membrane fluctuates in a random manner (see [14]).
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Meilikov, E., Farzetdinova, R. (2020). Bistable Perception of Ambiguous Images – Analytical Model. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_10
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