Undoubtedly, the sensory organs of biological systems have been evolved to accurately detect and locate the external stimuli, even if they are very weak. However, the mechanism underlying this ability is still not fully understood. Previously, it had been shown that stochastic resonance may be a good candidate to explain this ability, by which the response of a system to an external signal is amplified by the presence of noise. Recently, it is pointed out that the initial phase diversity in external signals can be also served as a simple and feasible mechanism for weak signal detection or amplification in excitable neurons. We here make a brief review on this progress. We will show that there are two kinds of effects of initial phase diversity: one is the phase disorder, i.e., the initial phases are different and static, and the other is the phase noise, i.e., the initial phases are time-varying like noise. Both cases show that initial phase diversity in subthreshold periodic signals can indeed play a constructive role in the emergence of sustained spiking activity. As initial phase diversity can mimic different arrival times from source signal to sensory organs, these findings may provide a cue for understanding the hunting behaviors of some biological systems.stochastic resonance, excitable neuron, phase noise, subthreshold signal Citation:Liang X M, Liu Z H. Effect of initial phase diversity on signal detection in excitable systems.

In many animals and insects, hearing is very acute to the faintest of sounds; the underlying mechanism can be explained by self-tuning. Recently, signal response amplification has been shown to be implemented through networks exhibiting scale-free topology, which has potential applications in artificial information processing systems and devices. We review in this paper the main results obtained in networked double-well oscillators and briefly discuss future research directions.signal response, scale-free network, self-tuning, double-well oscillator Citation:Liu Z H. Signal response amplification of scale-free networks. Chinese Sci Bull, 2011Bull, , 56: 3623-3629, doi: 10.1007 Systems that can detect and amplify signals at specific frequencies are commonplace in the natural world and most notably in the visual and auditory systems of animals [1]. Signal detection in animals is through light-and auralsensitive organs, constituted by a large number of networked units. For example, cells in living organisms respond to their environment by an interconnected network of receptors, messengers, protein kinases and other signaling molecules [2][3][4]. One of the more prominent features of our hearing system is the ability to perceive sound stimuli that range over six orders of magnitude in sound pressure [5]. Hair cells within the cochlear are stimulated by sound waves, the induced motion being amplified at characteristic locations that depends on the frequencies of sound. These cells transmit signals to the auditory nerve [6]. It is well known that many animals and insects have the ability to detect faint sounds from their environment. Physiological evidence exists for a range of animals and insect auditory systems that this active audition is due to Hopf bifurcations [7][8][9]. Models have also been proposed to develop the underlying mechanism behind enhanced amplification in hearing systems, i.e. self-tuned critical oscillations of hair cells nearby the Hopf bifurcation. 2 , where a controls the barrier height of the potential; x=±1 are the locations of the two minima. Suppose an oscillator has probability w + (w  ) of jumping from the right (left) well to the left (right) well; then self-tuning means that the parameter a can be self-adjusted by the following equation: a t a t w t p t w t p t twhere p + (p  ) denotes the probability that the oscillator stays at x=±1. If w + and w  are small, the first term in eq.(1) will be larger than the second term and thus result in a decrease in a. For larger w + and w  , the first term in eq.(1) will be smaller than the second term and thus result in an increase in a. Therefore, the parameter a is self-tuned to an optimal value by switching probabilities w + and w  . Scientists and engineers frequently take inspiration from

The study on complex networks has been recently obtained great success and is now focusing on the applications to other fields. In this review, we briefly introduce the progress of complex network theory on signal detection and transmission. We mainly summarize the results in three directions: (1) Signal amplification on complex networks; (2) signal detection on complex networks; (3) self-sustained oscillation in complex networks. These primary results make us understand the mechanism of neuron networks better, from the new angle of complex networks. It is also helpful for us to characterize the signal transmission from the neuron physical network to the brain functional network.