  As introduced by Bentley et al. (2005), artificial immune systems (AIS) are
lacking tissue, which is present in one form or another in all living
multi-cellular organisms. Some have argued that this concept in the context of
AIS brings little novelty to the already saturated field of the immune inspired
computational research. This article aims to show that such a component of an
AIS has the potential to bring an advantage to a data processing algorithm in
terms of data pre-processing, clustering and extraction of features desired by
the immune inspired system. The proposed tissue algorithm is based on
self-organizing networks, such as self-organizing maps (SOM) developed by
Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR)
affecting the activation function of the clusters developed by the SOM.
