Imitating the Brain with Neurocomputer A"New"Way Towards Artificial General Intelligence

To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the ＂new＂ approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.

NEUROCOMPUTATION OF VISUAL RELATIVE MOTION INFORMATION

In this paper the neurocomputation of figure-ground relative motion information in thevisual system of the fly have been investigated in great detail by a combination of quanti-tative behavioural experiments and computational model simulations. Only torque responsesabout the vertical axis of the tethered flying flies (Musca domestica) were determined inthe behavioural experiment. The main results of behavioural experiments are: (i) The dynam-ics of the torque responses depends not only on the phase relationship between figure andbackground motion but also on the oscillation frequency of the figure and ground. (ii) Inall the phase relations tested, the time courses are a characteristic fingerprint of the partic-ular phase relationship. (iii) The variation of the amplitude of the response peaks is an espe-cially sensitive indicator for the variability of figure--ground discrimination behaviour. The main results of computer simulations are: (i) The "computer fly", the networkmodel of both the SF-system