1. A computer implemented method in a self-adaptive multi modal data stream processing system having at least one computer processor and at least one spatiotemporal associative memory coupled to the at least one computer processor, the method comprising: constructing, by a construction module under control of a control module of the multi modal data stream processing system, a model of a situation built upon an underlying associative neural network that is partitioned into packets which are internally cohesive and externally weakly coupled subnetworks surrounded by energy barriers at a boundary of the subnetworks, wherein the underlying associative neural network is stored in the associative memory to establish situational understanding of the situation, and wherein the model is comprised of invariant and variable entities and relationships between the entities, wherein each entity is able to be nested by the control module by being comprised of lower level models and wherein the lower level models are formed of packets and are groups of packets; manipulating the lower level models by the control module of the multi modal data stream processing system, by manipulating packets while leaving the underlying associative neural network intact by not changing synaptic weights in the underlying associative neural network in the manipulation of the lower level models; and reducing, by the multi modal data stream processing system, energy consumption and energy dissipation in the constructing and the manipulating of the models by the control module seeking progressively more general and adequate models persisting through various situations and wherein the reducing energy consumption and dissipation translates into entropy reduction, or system negentropy production in the system.