Bio-inspired computing

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Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to 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. Biologically inspired computing is a major subset of natural computation.

Areas of research

Some areas of study encompassed under the canon of biologically inspired computing, and their biological counterparts:

genetic algorithms ↔ evolution
biodegradability prediction ↔ biodegradation
cellular automata ↔ life
emergent systems ↔ ants, termites, bees, wasps
neural networks ↔ the brain
artificial life ↔ life
artificial immune systems ↔ immune system
rendering (computer graphics) ↔ patterning and rendering of animal skins, bird feathers, mollusk shells and bacterial colonies
Lindenmayer systems ↔ plant structures
communication networks and protocols ↔ epidemiology and the spread of disease
membrane computers ↔ intra-membrane molecular processes in the living cell
excitable media ↔ forest fires, "the wave", heart conditions, axons, etc.
sensor networks ↔ sensory organs
learning classifier systems ↔ cognition, evolution

Artificial intelligence

The way in which bio-inspired computing differs from the traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to 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. Bio-inspired computing, on the other hand, takes a more bottom-up, decentralised 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. For example, training a virtual insect to navigate in an unknown terrain for finding food includes six simple rules. The insect is trained to

turn right for target-and-obstacle left;
turn left for target-and-obstacle right;
turn left for target-left-obstacle-right;
turn right for target-right-obstacle-left,
turn left for target-left without obstacle,
turn right for target right without obstacle.

The virtual insect controlled by the trained spiking neural network can find food after training in any unknown terrain. After several generations of rule application it is usually the case that some forms of complex behaviour 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 (see complex systems). 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.

Brain-inspired Computing

Brain-inspired computing refers to computational models and methods that are mainly based on the mechanism of the brain, rather than completely imitating the brain. The goal is to enable the machine to realize various cognitive abilities and coordination mechanisms of human beings in a brain-inspired manner, and finally achieve or exceed Human intelligence level.

The research status

Artificial intelligence researchers are now aware of the benefits of learning from the brain information processing mechanism. And the progress of brain science and neuroscience also provides the necessary basis for artificial intelligence to learn from the brain information processing mechanism.Brain and neuroscience researchers are also trying to apply the understanding of brain information processing to a wider range of science field. The development of the discipline benefits from the push of information technology and smart technology and in turn brain and neuroscience will also inspire the next generation of the transformation of information technology.

The influence of brain science on Brain-inspired computing

Advances in brain and neuroscience, especially with the help of new technologies and new equipment, support researchers to obtain multi-scale, multi-type biological evidence of the brain through different experimental methods, and are trying to reveal the structure of bio-intelligence from different aspects and functional basis. From the microscopic neurons, synaptic working mechanisms and their characteristics, to the mesoscopic network connection model, to the links in the macroscopic brain interval and their synergistic characteristics, the multi-scale structure and functional mechanisms of brains derived from these experimental and mechanistic studies will provide important inspiration for building a future brain-inspired computing model.

Brain-inspired chip

Broadly speaking, brain-inspired chip refers to a chip designed with reference to the structure of human brain neurons and the cognitive mode of human brain. Obviously, the "neuromorphic chip" is a brain-inspired chip that focuses on the design of the chip structure with reference to the human brain neuron model and its tissue structure, which represents a major direction of brain-inspired chip research. Along with the rise and development of “brain plans” in various countries, a large number of research results on neuromorphic chips have emerged, which have received extensive international attention and are well known to the academic community and the industry. For example, EU-backed SpiNNaker and BrainScaleS, Stanford's Neurogrid, IBM's TrueNorth, and Qualcomm's Zeroth.

References

Xu Z; Ziye X; Craig H; Silvia F (Dec 2013). "Spike-based indirect training of a spiking neural network-controlled virtual insect". Decision and Control (CDC), IEEE: 6798–6805. doi:10.1109/CDC.2013.6760966. ISBN 978-1-4673-5717-3.
Joshua E. Mendoza. ""Smart Vaccines" – The Shape of Things to Come". Research Interests. Archived from the original on November 14, 2012.

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