The poster by Mohamed Oubbati, Josef Frick, and Günther Palm presents a classic swarm behavioral model by C.W. Reynolds (1987, 1999) using neural fields (Amari, S. 1977) which are equivalent to continuous recurrent neural networks. This emergent grouping behaviour doesn't need any leaders, it's just a result of three steering behaviors – separation, cohesion and alignment – in every individual agent, "boid", which are adjusted in accordance of other boids and objects.
The poster itself doesn't really go very deep in the theory, it mainly consists of pictures of the steering behaviors and the emergent global behavior. The basic equations of a 1-D dynamic neural field (DNF) are shown, with the stimulus design of the steering behaviors. Every boid shares the same encoded steering stimulus design, which is quite intuitively determined. In the paper the authors have also presented the implementation in detail.
The results show that the swarm behavior can be implemented by neural fields. The worthiness of this practice still eludes me, and the choice of neural fields isn't elaborated anywhere. In comparison, the neural fields are very computational processes, and one can achieve the same results in object-oriented programming with ease. Still, the work is really an investigation and perhaps the use of neural fields can be extrapolated.