Generally, in nature, non-human creatures perform adaptive behaviors to external environment they are
living in. i.e. animals have to keep alive by improving their intelligent behavioral ability to be adaptable to their
living environmental conditions. This paper presents an investigational comparative overview on adaptive behaviors
associated with two diverse biological systems (Neural and Non-Neural). In more details, intelligent behavioral
performance of Ant Colony System (ACS) in order to reach optimal solution of Traveling Sales-man Problem (TSP)
is considered. That's investigated herein versus concepts of adaptive behavioral learning concerned with some
animals (cats, dogs, and rats), in order to keep survive. More precisely, investigations of behavioral observations
tightly related to suggested animals, supposed to obey discipline of biological information processing. So, Artificial
Neural Network (ANN) modeling is a relevant tool to investigate such biological system observations. Moreover, an
illustrative brief of ACS optimal intelligent behaviors to solve TSP is presented. Additionally, considering effect of
noisy environment on learning convergence, an interesting analogy between both proposed biological systems is
introduced. Finally, performance of three learning algorithms shown to be analogously in agreement with behavioral
concepts of both suggested biological systems' performance.
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