Bio-inspired computing
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
History
Early IdeasThe ideas behind biological computing trace back to 1936 and the first description of an abstract computer, which is now known as a Turing machine. Turing firstly described the abstract construct using a biological specimen. Turing imagined a mathematician that has three important attributes. He always has a pencil with an eraser, an unlimited number of papers and a working set of eyes. The eyes allow the mathematician to see and perceive any symbols written on the paper while the pencil allows him to write and erase any symbols that he wants. Lastly, the unlimited paper allows him to store anything he wants in memory. Using these ideas he was able to describe an abstraction of the modern digital computer. However Turing mentioned that anything that can perform these functions can be considered such a machine and he even said that even electricity should not be required to describe digital computation and machine thinking in general.
Neural Networks
First described in 1943 by Warren McCulloch and Walter Pitts, neural networks are a prevalent example of biological systems inspiring the creation of computer algorithms. They first mathematically described that a system of simplistic neurons was able to produce simple logical operations such as logical conjunction, disjunction and negation. They further showed that a system of neural networks can be used to carry out any calculation that requires finite memory. Around 1970 the research around neural networks slowed down and many consider a 1969 book by Marvin Minsky and Seymour Papert as the main cause. Their book showed that neural network models were able only model systems that are based on Boolean functions that are true only after a certain threshold value. Such functions are also known as threshold functions. The book also showed that a large amount of systems cannot be represented as such meaning that a large amount of systems cannot be modeled by neural networks. Another book by James Rumelhart and David McClelland in 1986 brought neural networks back to the spotlight by demonstrating the linear back-propagation algorithm something that allowed the development of multi-layered neural networks that did not adhere to those limits.
Ant Colonies
In 1979, Douglas Hofstadter described a biological system capable of performing intelligent computations even if the individual components were not intelligent. He used ant colonies as an example, showing that collective behavior can result in problem-solving abilities, a phenomenon known as emergent behavior. In 2009, Azimi et al. developed the ant colony algorithm, a clustering method that can determine the number of clusters and produce results competitive with traditional algorithms. Additionally, Hölder and Wilson analyzed historical data and concluded that ants have evolved to function as a single “superorganism” colony, which inspired further research into group-based evolutionary algorithm.
Areas of research
Some areas of study in biologically inspired computing, and their biological counterparts:| Bio-Inspired Computing Topic | Biological Inspiration |
| Evolutionary Algorithms | Evolution |
| Biodegradability prediction | Biodegradation |
| Cellular Automata | Life |
| Emergence | Ants, termites, bees, wasps |
| Artificial neural networks | Biological neural networks |
| Artificial life | Life |
| Artificial immune system | Immune system |
| Rendering | Patterning and rendering of animal skins, bird feathers, mollusk shells and bacterial colonies |
| Lindenmayer systems | Plant structures |
| Communication networks and communication protocols | Epidemiology |
| Membrane computers | Intra-membrane molecular processes in the living cell |
| Excitable media | Forest fires, "the wave", heart conditions, axons |
| Sensor networks | Sensory organs |
| Learning classifier systems | Cognition, evolution |
Population-Based Bio-Inspired Algorithms
Bio-inspired computing, which work on a population of possible solutions in the context of evolutionary algorithms or in the context of swarm intelligence algorithms, are subdivided into Population Based Bio-Inspired Algorithms. They include Evolutionary Algorithms, Particle Swarm Optimization, Ant colony optimization algorithms and Artificial bee colony algorithms.Virtual Insect Example
Bio-inspired computing can be used to train a virtual insect. The insect is trained to navigate in an unknown terrain for finding food equipped with six simple rules:- 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.
Natural evolution is a good analogy to this method–the rules of evolution 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.Research
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.TrueNorth is a brain-inspired chip that IBM has been developing for nearly 10 years. The US DARPA program has been funding IBM to develop pulsed neural network chips for intelligent processing since 2008. In 2011, IBM first developed two cognitive silicon prototypes by simulating brain structures that could learn and process information like the brain. Each neuron of a brain-inspired chip is cross-connected with massive parallelism. In 2014, IBM released a second-generation brain-inspired chip called "TrueNorth." Compared with the first generation brain-inspired chips, the performance of the TrueNorth chip has increased dramatically, and the number of neurons has increased from 256 to 1 million; the number of programmable synapses has increased from 262,144 to 256 million; Subsynaptic operation with a total power consumption of 70 mW and a power consumption of 20 mW per square centimeter. At the same time, TrueNorth handles a nuclear volume of only 1/15 of the first generation of brain chips. At present, IBM has developed a prototype of a neuron computer that uses 16 TrueNorth chips with real-time video processing capabilities. The super-high indicators and excellence of the TrueNorth chip have caused a great stir in the academic world at the beginning of its release.
In 2012, the Institute of Computing Technology of the Chinese Academy of Sciences and the French Inria collaborated to develop the first chip in the world to support the deep neural network processor architecture chip "Cambrian". The technology has won the best international conferences in the field of computer architecture, ASPLOS and MICRO, and its design method and performance have been recognized internationally. The chip can be used as an outstanding representative of the research direction of brain-inspired chips.