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Cellular neural networks have found applications both as a platform for high speed image processing, as well as a model for complex spatiotemporal dynamical phenomena. This paper describes spatial pattern formation in a CMOS VLSI implementation of a CNN. With four layers of 32×64 cells, this CNN contains the most recurrently interconnected 2D layers of any VLSI CNN implementation to date. With over 8000 cells, it is also one of the largest in terms of the total number of cells. The spatial patterns generated by this network are bands of activity with a preferred scale and orientation, both of which can be adjusted via external bias voltages. The VLSI implementation provides a physical substrate for experimenting with complex spatiotemporal phenomena in real time, while also being amenable to theoretical analysis. Our experimental characterization of the observed patterns are in excellent agreement with our theoretical predictions.

In this paper, we have investigated a spatial predator–prey model with hunting cooperation in predators. Using linear stability analysis, we obtain the condition for diffusive instability and identify the corresponding domain in the space of controlling parameters. Using extensive numerical simulations, we obtain complex patterns, namely, spotted pattern, stripe pattern and mixed pattern in the Turing domain, by varying the hunting cooperation parameter in predators and carrying capacity of preys. The results focus on the effect of hunting cooperation in pattern dynamics of a diffusive predator–prey model and help us in better understanding of the dynamics of the predator–prey interaction in real environments.