Biologically Inspired Intelligence(BII) also named Bio-inspired computing, short for biologically inspired computing a field of study that loosely knits together subfields related to the topics of connectionism, social behavior and emergence.

The main goal of BII is, to get artificial Intelligence (AI) as close as possible to Human-kind behavior. It differs from traditional machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model the living phenomena, and simultaneously the study of life to improve the usage of computers.

The way in which BII differs from the traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to the what could be described as 'creationist' methods used in traditional AI. In traditional AI, intelligence is often programmed from above: the programmer is the creator, and makes something and imbues it with its intelligence. BII on the other hand, takes a more bottom-up, decentralized approach; bio-inspired techniques often involve the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules, and a method of iteratively applying those rules.

After several generations of rule application, it is usually the case that some forms of complex behavior arise. Complexity gets built upon complexity until the end result is something markedly complex, and quite often completely counterintuitive from what the original rules would be expected to produce. For this reason, in neural network models, it is necessary to accurately model an in vivo network, by live collection of "noise" coefficients that can be used to refine statistical inference and extrapolation as system complexity increases.

Natural evolution is a good analogy to this method–the rules of evolution (selection, recombination/reproduction, mutation and more recently transposition) are in principle simple rules, yet over millions of years have produced remarkably complex organisms. A similar technique is used in genetic algorithms. (see also Wikipedia)

The combination of spiking neural nets, genetic algorithms, statistics and common mathematics creates a new quality of AI which gets much closer to human-behavior also called human-like, or as we say, biologically inspired intelligence.