B9: Attractor Networks and the Dynamics of Visual Perception J Braun1, G Deco2 |
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1Otto-von-Guericke-University Magdeburg, Germany |
| First principles of statistical inference suggest (e.g., Friston, Breakspear, Deco, 2012) that visual perception relies on two interaction loops: a fast ‘recognition loop’ to match retinal input and memorized world models and a slow ‘learning loop’ to improve these world models. Focusing on the fast loop, we try to make these abstract notions fruitful in terms of novel experimental paradigms and observations. The first half of the tutorial reviews the activity dynamics of attractor networks at different space-time scales – especially mesoscopic models of cortical columns and groups of columns and macroscopic models of whole-brain dynamics – and the second half compares the dynamics of perceptual decisions in the context of choice tasks, multi-stable percepts, and cooperative percepts. We argue that only a combination of principled models of collective neural dynamics and careful empirical studies of perceptual dynamics can guide us towards a fuller understanding of the principles and mechanisms of visual inference. |
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