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ne 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 edit 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 virtual insect example edit 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 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 edit 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 edit 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 edit 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 brain inspired chip edit 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 unclear brain mechanism cognition edit 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 unclear brain inspired computational models and algorithms edit 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 edit 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 see also edit this article is in list format but may read better as prose you can help by converting this article if appropriate editing help is available december 2016 applications of artificial intelligence behavior based robotics bioinformatics bionics cognitive architecture cognitive modeling cognitive science connectionism digital morphogenesis digital organism 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 neuroevolution olaf sporns organic computing unconventional computing lists list of emerging technologies outline of artificial intelligence references edit turing alan 1936 on computable numbers with an application to the entscheidungsproblem mathematical society oclc 18386775 turing alan 2004 09 09 computing machinery and intelligence 1950 the essential turing oxford university press pp 433 464 doi 10 1093 oso 9780198250791 003 0017 isbn 978 0 19 825079 1 retrieved 2022 05 05 mcculloch warren pitts walter 2021 02 02 a logical calculus of the ideas immanent in nervous activity 1943 ideas that created the future the mit press pp 79 88 doi 10 7551 mitpress 12274 003 0011 isbn 9780262363174 s2cid 262231397 retrieved 2022 05 05 minsky marvin 1988 perceptrons an introduction to computational geometry the mit press isbn 978 0 262 34392 3 oclc 1047885158 history the past userweb ucs louisiana edu retrieved 2022 05 05 mcclelland james l rumelhart david e 1999 parallel distributed processing explorations in the microstructure of cognition mit press isbn 0 262 18120 7 oclc 916899323 wilson david sloan sober elliott 1989 02 08 reviving the superorganism journal of theoretical biology 136 3 337 356 bibcode 1989jthbi 136 337w doi 10 1016 s0022 5193 89 80169 9 issn 0022 5193 pmid 2811397 azimi javad cull paul fern xiaoli 2009 clustering ensembles using ants algorithm in mira josé ferrández josé manuel álvarez josé r de la paz félix toledo f javier eds methods and models in artificial and natural computation a homage to professor mira s scientific legacy lecture notes in computer science vol 5601 berlin heidelberg springer pp 295 304 doi 10 1007 978 3 642 02264 7_31 isbn 978 3 642 02264 7 wilson david sloan sober elliott 1989 02 08 reviving the superorganism journal of theoretical biology 136 3 337 356 bibcode 1989jthbi 136 337w doi 10 1016 s0022 5193 89 80169 9 issn 0022 5193 pmid 2811397 farinati davide vanneschi leonardo december 2024 a survey on dynamic populations in bio inspired algorithms genetic programming and evolvable machines 25 2 19 doi 10 1007 s10710 024 09492 4 hdl 10362 170138 xu z ziye x craig h silvia f dec 2013 spike based indirect training of a spiking neural network controlled virtual insect 52nd ieee conference on decision and control pp 6798 6805 citeseerx 10 1 1 671 6351 doi 10 1109 cdc 2013 6760966 isbn 978 1 4673 5717 3 s2cid 13992150 joshua e mendoza smart vaccines the shape of things to come research interests archived from the original on november 14 2012 徐波 刘成林 曾毅 类脑智能研究现状与发展思考 j 中国科学院院刊 2016 31 7 793 802 美国类脑芯片发展历程 electronic engineering product world chen tianshi du zidong sun ninghui wang jia wu chengyong chen yunji temam olivier 2014 dian nao acm sigarch computer architecture news 42 269 284 doi 10 1145 2654822 2541967 markram henry muller eilif ramaswamy srikanth reconstruction and simulation of neocortical microcircuitry j cell 2015 vol 163 2 pp 456 92pubmed schirner michael deco gustavo ritter petra 2023 learning how network structure shapes decision making for bio inspired computing nature communications 14 2963 2963 bibcode 2023natco 14 2963s doi 10 1038 s41467 023 38626 y pmc 10206104 pmid 37221168 further reading edit the following are presented in ascending order of complexity and depth with those new to the field suggested to start from the top nature inspired algorithms biologically inspired computing dead link 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 o...