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
Artificial intelligence
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. 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
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.
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.
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.
