Some architectures of neural networks with temporal effects

Following a new paradigm of information encoding by spike timings and its processing by neurons as coincidence detectors, we first discuss some aspects of temporal neural phenomena, and give an evolutionary interpretation of the relationships between the axon diameter, propagation speed and density of neural tissue. Then we propose a recurrent architecture of neural network capable to convert periodic spike train into desired pattern of spike timings. Another configuration that we propose represent neural fiber as a delay element where the changeable delay effect is attained over lateral loops with creeping synapses which shortcut the spanned portions of the basic fiber. As the starting and termination might represent important indicators of a spike burst we also propose the structure of a neural differentiator with cross inhibition. Finally, we give the internal structure of a neural delay element with an incremental change of delay value, including an explanation of changing, i.e. the learning process.