Visual perception of ambiguous figures: synchronization based neural models

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin–Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts. This article is part of a special issue on Neuronal Dynamics of Sensory Coding. This special issue is in honour of Professor Pepe Segundo who is one of the pioneers in the study of neural coding. Pepe has been an active participant in many Neural Coding Workshops sharing his great knowledge and experience of research in this field. I (R. Borisyuk) was very happy to meet Pepe for the first time in Prague when attending the first Neural Coding Workshop in 1995. From that time we regularly met at Neural Coding Workshops and these meetings have always been very stimulating and fruitful for my research. Remarkably, the first paper I studied at the beginning of my scientific career was a seminal paper by Moore et al. (1970). For me, this paper provided a great opportunity to learn the basic statistical techniques for the analysis of multiple spike trains and neural coding. According to the Institute of Scientific Information, this paper has been cited 380 times! This exciting paper has inspired my research into the synaptic and functional connectivity of neural circuits derived from spike-train recordings (Borisyuk et al. 1985; Stuart et al. 2005) and guided my search for new ideas on neural coding.