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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
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 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.
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(CAS) 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.
The problem Brain-inspired Computing are facing
- Unclear Brain mechanism cognition
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.
- Unclear Brain-inspired computational models and algorithms
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.
- Constrained Computational architecture and capabilities
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.
This article is in a list format that may be better presented using prose. (December 2016)
- Applications of artificial intelligence
- Artificial life
- Artificial neural network
- Behavior based robotics
- Cognitive architecture
- Cognitive modeling
- Cognitive science
- Digital morphogenesis
- Digital organism
- Evolutionary algorithm
- Evolutionary computation
- Fuzzy logic
- Gene expression programming
- Genetic algorithm
- Genetic programming
- Gerald Edelman
- Janine Benyus
- Learning classifier system
- Mark A. O'Neill
- Mathematical biology
- Mathematical model
- Natural computation
- Olaf Sporns
- Organic computing
- Swarm intelligence
- 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. pp. 6798–6805. CiteSeerX 10.1.1.671.6351. 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.
- Chen T, Du Z, Sun N, et al. Diannao: A small-footprint high throughput accelerator for ubiquitous machine-learning//ACM Sigplan Notices. New York: ACM, 2014, 49(4): 269-284
- Markram Henry , Muller Eilif , Ramaswamy Srikanth Reconstruction and simulation of neocortical microcircuitry [J].Cell, 2015, Vol.163 (2), pp.456-92PubMed
(the following are presented in ascending order of complexity and depth, with those new to the field suggested to start from the top)
- "Biologically Inspired Computing"
- "Digital Biology", Peter J. Bentley.
- "First International Symposium on Biologically Inspired Computing"
- Emergence: The Connected Lives of Ants, Brains, Cities and Software, Steven Johnson.
- Dr. Dobb's Journal, Apr-1991. (Issue theme: Biocomputing)
- Turtles, Termites and Traffic Jams, Mitchel Resnick.
- Understanding Nonlinear Dynamics, Daniel Kaplan and Leon Glass.
- Ridge, E.; Kudenko, D.; Kazakov, D.; Curry, E. (2005). "Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments". Self-Organization and Autonomic Informatics (I). 135: 35–49. CiteSeerX 10.1.1.64.3403.
- Swarms and Swarm Intelligence by Michael G. Hinchey, Roy Sterritt, and Chris Rouff,
- Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, L. N. de Castro, Chapman & Hall/CRC, June 2006.
- "The Computational Beauty of Nature", Gary William Flake. MIT Press. 1998, hardcover ed.; 2000, paperback ed. An in-depth discussion of many of the topics and underlying themes of bio-inspired computing.
- Kevin M. Passino, Biomimicry for Optimization, Control, and Automation, Springer-Verlag, London, UK, 2005.
- Recent Developments in Biologically Inspired Computing, L. N. de Castro and F. J. Von Zuben, Idea Group Publishing, 2004.
- Nancy Forbes, Imitation of Life: How Biology is Inspiring Computing, MIT Press, Cambridge, MA 2004.
- M. Blowers and A. Sisti, Evolutionary and Bio-inspired Computation: Theory and Applications, SPIE Press, 2007.
- X. S. Yang, Z. H. Cui, R. B. Xiao, A. H. Gandomi, M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, 2013.
- "Biologically Inspired Computing Lecture Notes", Luis M. Rocha
- The portable UNIX programming system (PUPS) and CANTOR: a computational envorionment for dynamical representation and analysis of complex neurobiological data, Mark A. O'Neill, and Claus-C Hilgetag, Phil Trans R Soc Lond B 356 (2001), 1259–1276
- "Going Back to our Roots: Second Generation Biocomputing", J. Timmis, M. Amos, W. Banzhaf, and A. Tyrrell, Journal of Unconventional Computing 2 (2007) 349–378.
- Neumann, Frank; Witt, Carsten (2010). Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity. Natural Computing Series. Berlin: Springer-Verlag. ISBN 978-3-642-16543-6. Zbl 1223.68002.
- Brabazon, Anthony; O’Neill, Michael (2006). Biologically inspired algorithms for financial modelling. Natural Computing Series. Berlin: Springer-Verlag. ISBN 978-3-540-26252-7. Zbl 1117.91030.
- C-M. Pintea, 2014, Advances in Bio-inspired Computing for Combinatorial Optimization Problem, Springer ISBN 978-3-642-40178-7
- "PSA: A novel optimization algorithm based on survival rules of porcellio scaber", Y. Zhang and S. Li
- Nature Inspired Computing and Engineering (NICE) Group, University of Surrey, UK
- ALife Project in Sussex
- Biologically Inspired Computation for Chemical Sensing Neurochem Project
- AND Corporation
- Centre of Excellence for Research in Computational Intelligence and Applications Birmingham, UK
- BiSNET: Biologically-inspired architecture for Sensor NETworks
- BiSNET/e: A Cognitive Sensor Networking Architecture with Evolutionary Multiobjective Optimization
- Biologically inspired neural networks
- NCRA UCD, Dublin Ireland
- The PUPS/P3 Organic Computing Environment for Linux
- SymbioticSphere: A Biologically-inspired Architecture for Scalable, Adaptive and Survivable Network Systems
- The runner-root algorithm
- Bio-inspired Wireless Networking Team (BioNet)
- Biologically Inspired Intelligence