Биологиядан шабыттанған есептеулер

using biology as an inspiration in 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.

Bio Inspired computing can be distinguished from traditional artificial intelligence by its approach to computer learning. Bio inspired computing uses an evolutionary approach, while traditional A. I. uses a 'creationist' approach. Bio inspired computing begins with a set of simple rules and simple organisms which adhere to those rules. Over time, these organisms evolve within simple constraints. This method could be considered bottom up or decentralized. In traditional artificial intelligence, intelligence is often programmed from above: the programmer is the creator, and makes something and imbues it with its intelligence.

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.

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 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. 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.

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.