On Principles 0f Biological Information Processing Concerned with Learning Convergence Mechanism in Neural and Non-Neural Bio-Systems

This article presents an investigational overview for principles of biological information processing. That is fulfilled through analysis of some learning case examples for both neural and non-neural biological systems. Mainly, this work concerned with learning performance curves observed after some experimental work and mathematical simulation programming. Three learning case examples are considered, two of them belong to neural bio-systems. Both are associated to results of some animals psych-learning experiments. Third case example belongs to non-neural bio-systems. That is ant colony system (ACS) leaning how to solve optimally minimum path problem to survive (food storage process). Mathematical formulation of (ACS) behavior is presented after computational biology of optimum solution for traveling salesman problem. The deduced formula shown to be very similar to sigmoid activation function. That is used commonly in artificial neural network ANNs models. Biological information processing for all examples (and more other animal learning work) shown to obey commonly least mean square (LMS) error algorithm with variations of learning rate parameter. Finally, some interesting conclusive remarks, and interesting analogous features (for learning mechanisms) among examples are presented