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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.
Early Ideas
The ideas behind biological computing trace back to 1936 with 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.[1] 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.[2]
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.[3] 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.[4][5] 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.[6]
Ant Colonies
In 1979, Douglas Hofstadter described a biological system capable of performing intelligent computations even if the individual components were not intelligent.[7] He used ant colonies as an example, showing that collective behavior can result in problem-solving abilities, a phenomenon known as emergent behavior.[8] 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.[9] Additionally, Hölder and Wilson (2009) 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.
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 (computer graphics) | 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 |
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 (PBBIA).[10] They include Evolutionary Algorithms, Particle Swarm Optimization, Ant colony optimization algorithms and Artificial bee colony algorithms.
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:
The virtual insect controlled by the trained spiking neural network can find food after training in any unknown terrain.[11] After several generations of rule application it is usually the case that some forms of complex behaviour emerge. Complexity gets built upon complexity until the 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, when modeling the neural network, 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.[12]
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 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.
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.
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.[13]
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.[14] 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(CAS) and the French Inria collaborated to develop the first chip in the world to support the deep neural network processor architecture chip "Cambrian".[15] 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.
The human brain is a product of evolution. Although its structure and information processing mechanism are constantly optimized, compromises in the evolution process are inevitable. The cranial nervous system is a multi-scale structure. There are still several important problems in the mechanism of information processing at each scale, such as the fine connection structure of neuron scales and the mechanism of brain-scale feedback. Therefore, even a comprehensive calculation of the number of neurons and synapses is only 1/1000 of the size of the human brain, and it is still very difficult to study at the current level of scientific research.[16] Recent advances in brain simulation linked individual variability in human cognitive processing speed and fluid intelligence to the balance of excitation and inhibition in structural brain networks, functional connectivity, winner-take-all decision-making and attractor working memory.[17]
In the future research of cognitive brain computing model, it is necessary to model the brain information processing system based on multi-scale brain neural system data analysis results, construct a brain-inspired multi-scale neural network computing model, and simulate multi-modality of brain in multi-scale. Intelligent behavioral ability such as perception, self-learning and memory, and choice. Machine learning algorithms are not flexible and require high-quality sample data that is manually labeled on a large scale. Training models require a lot of computational overhead. Brain-inspired artificial intelligence still lacks advanced cognitive ability and inferential learning ability.
Most of the existing brain-inspired chips are still based on the research of von Neumann architecture, and most of the chip manufacturing materials are still using traditional semiconductor materials. The neural chip is only borrowing the most basic unit of brain information processing. The most basic computer system, such as storage and computational fusion, pulse discharge mechanism, the connection mechanism between neurons, etc., and the mechanism between different scale information processing units has not been integrated into the study of brain-inspired computing architecture. Now an important international trend is to develop neural computing components such as brain memristors, memory containers, and sensory sensors based on new materials such as nanometers, thus supporting the construction of more complex brain-inspired computing architectures. The development of brain-inspired computers and large-scale brain computing systems based on brain-inspired chip development also requires a corresponding software environment to support its wide application.
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(the following are presented in ascending order of complexity and depth, with those new to the field suggested to start from the top)
{{cite journal}}: Cite uses deprecated parameter |citeseerx= (help)Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
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.
Health informatics is the study and implementation of computer science to improve communication, understanding, and management of medical information. It can be viewed as a branch of engineering and applied science. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques. Health informatics is a multidisciplinary field that includes the study of the design, development, and application of computational innovations to improve health care. The disciplines involved combine healthcare fields with computing fields, in particular computer engineering, software engineering, information engineering, bioinformatics, bio-inspired computing, theoretical computer science, information systems, data science, information technology, autonomic computing, and behavior informatics. In academic institutions, health informatics includes research focused on applications of artificial intelligence in healthcare and designing medical devices based on embedded systems. In some countries the term informatics is also used in the context of applying library science to data management in hospitals where it aims to develop methods and technologies for the acquisition, processing, and study of patient data, An umbrella term of biomedical informatics has been proposed.
Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality. The concept was put forward by Per Bak, Chao Tang and Kurt Wiesenfeld ("BTW") in a paper following an earlier paper by Jonathan I. Katz published in 1987 in Physical Review Letters, and is considered to be one of the mechanisms by which complexity arises in nature. Its concepts have been applied across fields as diverse as geophysics, physical cosmology, evolutionary biology and ecology, bio-inspired computing and optimization (mathematics), economics, quantum gravity, sociology, solar physics, plasma physics, neurobiology and others. SOC is typically observed in slowly driven non-equilibrium systems with many degrees of freedom and strongly nonlinear dynamics. Many individual examples have been identified since BTW's original paper, but to date there is no known set of general characteristics that guarantee a system will display SOC.
The BioWall is a bio-inspired computing surface made of several thousand electronic modules which can be seen as artificial molecules. Each of these modules contains a programmable electronic circuit, a touch sensor and a display composed of 64 LEDs (Light-emitting diodes). As a result, each module enables the visitor to communicate with the surface by touching it with their finger, calculates its new status and indicates it immediately on a coloured display.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
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