Editing Bio-inspired computing (section)

== Areas of research ==

Some areas of study in biologically inspired computing, and their biological counterparts:
{| class="wikitable"
|+
!Bio-Inspired Computing Topic
!Biological Inspiration
|-
|[[Evolutionary Algorithm]]s
|[[Evolution]]
|-
|[[Biodegradability prediction]]
|[[Biodegradation]]
|-
|[[Cellular automaton|Cellular Automata]]
|[[Life]]
|-
|[[Emergence]]
|[[Ant]]s, [[termite]]s, [[bee]]s, [[wasp]]s
|-
|[[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 protocol]]s
|Epidemiology
|-
|[[P system|Membrane computers]]
|[[Cell membrane|Intra-membrane]] [[Molecular biology|molecular]] processes in the [[Cell (biology)|living cell]]
|-
|[[Excitable medium|Excitable media]]
|[[Wildfire|Forest fires]], [[Audience wave|"the wave"]], [[Tachycardia|heart conditions]], [[axon]]s
|-
|[[Sensor networks]]
|[[Sensory organs]]
|-
|[[Learning classifier system]]s
|[[Cognition]], [[evolution]]
|}

===Population Based Bio-Inspired Algorithms===
Bio-inspired computing, which work on a population of possible solutions in the context of [[evolutionary algorithm]]s or in the context of [[swarm intelligence]] algorithms, are subdivided into '''Population Based Bio-Inspired Algorithms''' (PBBIA).<ref>{{cite journal |last1=Farinati |first1=Davide |last2=Vanneschi |first2=Leonardo |title=A survey on dynamic populations in bio-inspired algorithms |journal=Genetic Programming and Evolvable Machines |date=December 2024 |volume=25 |issue=2 |doi=10.1007/s10710-024-09492-4|hdl=10362/170138 |hdl-access=free }}</ref> They include [[Evolutionary Algorithm]]s, [[Particle Swarm Optimization]], [[Ant colony optimization algorithms]] and [[Artificial bee colony algorithm]]s.

==== 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.
The virtual insect controlled by the trained [[spiking neural network]] can find food after training in any unknown terrain.<ref name="Silvia_2013">{{cite book | author = Xu Z |author2=Ziye X |author3=Craig H |author4=Silvia F |title=52nd IEEE Conference on Decision and Control |chapter=Spike-based indirect training of a spiking neural network-controlled virtual insect | journal = IEEE Decision and Control | pages = 6798–6805 |date=Dec 2013 | doi = 10.1109/CDC.2013.6760966 | isbn = 978-1-4673-5717-3 |citeseerx=10.1.1.671.6351 |s2cid=13992150 }}</ref> After several generations of rule application it is usually the case that some forms of complex behaviour [[Emergence|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 system]]s). For this reason, when modeling the [[neural network (machine learning)|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.<ref>{{cite web|url=http://www.duke.edu/~jme17/Joshua_E._Mendoza-Elias/Research_Interests.html#Neuroscience_-_Neural_Plasticity_in|title="Smart Vaccines" – The Shape of Things to Come|author=Joshua E. Mendoza|work=Research Interests|archiveurl=https://web.archive.org/web/20121114233853/http://people.duke.edu/~jme17/Joshua_E._Mendoza-Elias/Research_Interests.html|archivedate=November 14, 2012}}</ref>

Natural evolution is a good analogy to this method–the rules of evolution ([[Selection (biology)|selection]], [[Genetic recombination|recombination]]/reproduction, [[mutation]] and more recently [[transposition (genetics)|transposition]]) are in principle simple rules, yet over millions of years have produced remarkably complex organisms. A similar technique is used in [[genetic algorithm]]s.

=== 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 ====
[[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 [[Connectionism|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.<ref>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.</ref>

==== 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 (software)|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&nbsp;mW and a power consumption of 20&nbsp;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.<ref>{{cite web|url=http://www.eepw.com.cn/article/271641.htm|title=美国类脑芯片发展历程|publisher=[[Electronic Engineering & Product World]]}}</ref> 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".<ref>{{cite journal | doi=10.1145/2654822.2541967 | title=Dian ''Nao'' | year=2014 | last1=Chen | first1=Tianshi | last2=Du | first2=Zidong | last3=Sun | first3=Ninghui | last4=Wang | first4=Jia | last5=Wu | first5=Chengyong | last6=Chen | first6=Yunji | last7=Temam | first7=Olivier | journal=ACM SIGARCH Computer Architecture News | volume=42 | pages=269–284 | doi-access=free }}</ref> 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 ====
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.<ref>Markram Henry, Muller Eilif, Ramaswamy Srikanth [https://www.sciencedirect.com/science/article/pii/S0092867415011915 Reconstruction and simulation of neocortical microcircuitry] [J].Cell, 2015, Vol.163 (2), pp.456-92PubMed</ref>
Recent advances in brain simulation linked individual variability in human cognitive [[Mental chronometry|processing speed]] and [[Fluid and crystallized intelligence|fluid intelligence]] to the [[Homeostasis#Neurotransmission|balance of excitation and inhibition]] in [[Connectome|structural brain networks]], [[Resting state fMRI#Functional|functional connectivity]], [[Winner-take-all (computing)|winner-take-all decision-making]] and [[Dynamical neuroscience#Attractor network|attractor]] [[working memory]].<ref>{{Cite journal |last1=Schirner |first1=Michael |last2=Deco |first2=Gustavo |last3=Ritter |first3=Petra |date=2023 |title=Learning how network structure shapes decision-making for bio-inspired computing |journal=Nature Communications |volume=14 |issue=2963 |page=2963 |doi=10.1038/s41467-023-38626-y|pmid=37221168 |pmc=10206104 |bibcode=2023NatCo..14.2963S }}</ref>

==== 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.