Can Self-Organization Emerge through Dynamic Neural Fields Computation?

International audienceIn this paper, dynamic neural fields are used to develop key features of a cortically-inspired computational module. Under the perspective of designing computational systems that can exhibit the flexibility and genericity of the cortical substrate, using neural field as the competition layer for self-organizing modules has to be considered. However, despite the fact that they serve as a biologically-inspired model, applying dynamic neural fields to drive self-organization is not straightforward. In order to address that issue, an original method for evaluating neural field equations is proposed, based on statistical measurements of the field behavior in some scenarios. Limitations of classical neural field equations are then quantified, and an original field equation is proposed to overcome these difficulties. The performance of the proposed field model is discussed in comparison with some previously considered models, leading to the promotion of the proposed model as a suitable mean for processing competition in cortex-like computation for cognitive systems

Making competition in neural fields suitable for computational architectures

In this paper, a new competition mechanism for neural fields is proposed, as well as first experimental studies of its robustness. The computational properties of this algorithm are discussed, arguing that such properties are suitable for neural architectures, where some restrictions of the usual neural fields competition methods are not acceptable