Decision Networks Cannot Achieve Optimal Performance due to Biological Constraints

Decision making is important basic mechanism of intelligent behavior. It is valid especially for studying higher brain functions as a tool to achieve an asymptotically optimal performance. Level of decision networks performance could determine the efficiency in most categories of human choice processes. We argue that during adaptation there are serious biological constraints in neural networks limiting mediation of the choice processes parameters. Evidence is corrupted by noise and reward during trade-off in units of log (P) probabilities. As result, randomness and informational entropy is part of the decision process itself. We analyze the mechanisms involved in neural computations with a view toward development of novel computational paradigms based on how the brain works.