Despite being successfully used in the design of various biologically-inspired applications, the paradigm of dynamic neural fields (DNF) does not seem to have been exploited at its full potential yet. Partly because of the difficulties concerning a comprehensive theoretical study of them, essential aspects as learning mechanisms have rarely been addressed in the literature. In the current paper, we first show that classical DNF equations fail to offer reliable support for self-organization, unveiling some behavioural issues that prevent the fields to achieve this goal. Then, as an alternative to these, we propose a new DNF equation capable of deploying indeed a self-organizing mechanism based on neural fields.