At the head of applications of CVNNs in engineering, we have coherent systems,
in particular, sensing and imaging systems. Our daily life widely depends
on radars and sonars where we use coherent waves to probe the world. Conventional
systems treat information obtained by such systems just as they do for
other types of image data. However, we can develop various new functions
by processing the image by appropriate CVNNs utilizing the fact that the
information arose from wave phenomena.
In the future, the CVNNs will become the most fundamental framework to
realize quantum-wave electronic devices having learning and/or self-organizing
dynamics. VLSI technology advances more miniaturization. Then we face to
crystal inhomogeneity and temporal instability in the bit processing, i.e.,
the difficulty in holding “1” or “0” stably. In such a stochastic world,
the neural networks make a significant contribution to the electronic devices
as the most fundamental information-processing framework. Ultimately, in
addition, such devices deal with electron-wave, maybe with its spin. We
therefore require phase sensitive learning and self-organizing dynamics,
i.e., the CVNNs. Related fields includes super-conducting devices, quantum
neural devices, and learning optical computing.
In computer graphics and virtual reality, we inevitably need quaternion
neural networks as we mentioned the background. In deep space missions,
the quaternion networks is indispensable to control thrust and robot arms
since a command leaving the earth requires long period before it reaches
the spaceship. We need a self-reliant system based on quaternion neural
In addition, the phase information increases its significance even in physiology to elucidate the interaction between macroscopic potential wave and microscopic neuron firing. An example is the relationship between the neural spike timing with reference to the theta wave and the short-term memory.
Figure 1 summarizes possible application fields and their basis [A.Hirose, “Complex-Valued
Neural Networks,” Springer (2006)].