Astroglial-neural networks, diffusion-enhancement bilayers, and spatiotemporal grouping dynamics

A network is described that can be used for multiple targets grouping and tracking or directing a vision system's focus of attention. The network models a biologically plausible astroglial- neural network in the visual cortex whose parameters are tuned to match a psychophysical database on apparent motion. The architecture consists of a diffusion layer and a contrast- enhancement layer coupled by feedforward and feedback connections; input is provided by a separate feature extracting layer. The dynamics of the diffusion-enhancement bilayer exhibit grouping of static features on multiple scales as a function of time, and long-range apparent motion between time varying inputs. The model is cast as a parallel analog circuit which is realizable in VLSI. We present simulations that reproduce static grouping phenomena useful for multiple target grouping and tracking over multiple scales, demonstrate several long-range apparent motion phenomena, and discuss single targets that split, and multiple targets that merge.