They further showed that a system of neural networks can be used to carry out any calculation that requires finite memory.
Around 1970 the research around neural networks slowed down and many consider a 1969 book by Marvin Minsky and Seymour Papert as the main cause.
[8] Lastly Hölder and Wilson in 2009 concluded using historical data that ants have evolved to function as a single "superogranism" colony.
Artificial intelligence researchers are now aware of the benefits of learning from the brain information processing mechanism.
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.
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.
The US DARPA program has been funding IBM to develop pulsed neural network chips for intelligent processing since 2008.
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.
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.
Machine learning algorithms are not flexible and require high-quality sample data that is manually labeled on a large scale.
(the following are presented in ascending order of complexity and depth, with those new to the field suggested to start from the top)