The immune system as a neural network: a multi-epitope approach.

The term "neural network" has been applied to arrays of simple activation units linked by weighted connections. If the connections are modified according to a defined learning algorithm, such networks can be trained to store and retrieve patterned information. Memories are distributed throughout the network, allowing the network to recall complete patterns from incomplete input (pattern completion). The major biological application of neural network theory to date has been in the neurosciences, but the immune system may represent an alternative organ system in which to search for neural network architecture. Previous applications of parallel distributed processing to idiotype network theory have focused upon the recognition of individual epitopes. We argue here that this approach may be too restrictive, underestimating the power of neural network architecture. We propose that the network stores and retrieves large, complex patterns consisting of multiple epitopes separated in time and space. Such a network would be capable of perceiving an entire bacterium, and of storing the time course of a viral infection. While recognition of solitary epitopes occurs at the cellular level in this model, recognition of structures larger than the width of an antibody binding site takes place at the organ level, via network architecture integration of, i.e. individual epitope responses. The Oudin-Cazenave enigma, the sharing of idiotypic determinants by antibodies directed against distinct regions of the same antigen, suggests that some network level of integration of the individual clonal responses to large antigens does occur. The role of cytokines in prior neural network models of the immune system is unclear. We speculate that cytokines may influence the temperature of the network, such that changes in the cytokine milieu serve to "anneal" the network, allowing it to achieve the optimum steady-state in the shortest period of time.