Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network.

Characterizing Quantum Effects in Optically Induced Nanowire Self-Oscillations: Coherent Properties

Mechanical properties of metallic-nanowire self-oscillations are investigated through a coherent-state analysis. We focus on elucidating the time behavior of quantum energy in such oscillations, in addition to the analysis of fluctuations, evolution of eigenstates, and oscillatory trajectories. The quantum energy varies somewhat randomly at first, but, at a later time, it undergoes a stable periodical oscillation; the mean energy in the stabilized motion is large when the frequency of the driving force is resonated with that of the intrinsic oscillation of the nanowire. We confirmed that when the oscillatory amplitude is sufficiently low, the quantum energy is quite different from the classical one due to zero-point energy, which appears in the quantum regime. Because the power in such an oscillation is typically ultra low, quantum effects in the nanowire oscillations are non-negligible. Detailed analysis for the evolution of the probability densities and their relation with the oscillation trajectories of the nanowire are also carried out. Characterizing quantum effects in the actual oscillatory motions and clarifying their difference from the classical ones are important in understanding nanowire self-oscillations.

An Intrinsic Oscillation of Gene Networks Inside Hair Follicle Stem Cells: An Additional Layer That Can Modulate Hair Stem Cell Activities

This article explores and summarizes recent progress in and the characterization of main players in the regulation and cyclic regeneration of hair follicles. The review discusses current views and discoveries on the molecular mechanisms that allow hair follicle stem cells (hfSCs) to synergistically integrate homeostasis during quiescence and activation. Discussion elaborates on a model that shows how different populations of skin stem cells coalesce intrinsic and extrinsic mechanisms, resulting in the maintenance of stemness and hair regenerative potential during an organism’s lifespan. Primarily, we focus on the question of how the intrinsic oscillation of gene networks in hfSCs sense and respond to the surrounding niche environment. The review also investigates the existence of a cell-autonomous mechanism and the reciprocal interactions between molecular signaling axes in hfSCs and niche components, which demonstrates its critical driving force in either the activation of whole mini-organ regeneration or quiescent homeostasis maintenance. These exciting novel discoveries in skin stem cells and the surrounding niche components propose a model of the intrinsic stem cell oscillator which is potentially instructive for translational regenerative medicine. Further studies, deciphering of the distribution of molecular signals coupled with the nature of their oscillation within the stem cells and niche environments, may impact the speed and efficiency of various approaches that could stimulate the development of self-renewal and cell-based therapies for hair follicle stem cell regeneration.

Itinerant complexity in networks of intrinsically bursting neurons

Active neurons can be broadly classified by their intrinsic oscillation patterns into two classes characterized by periodic spiking or periodic bursting. Here we show that networks of identical bursting neurons with inhibitory pulsatory coupling exhibit itinerant dynamics. Using the relative phases of bursts between neurons, we numerically demonstrate that the network exhibits endogenous transitions among multiple modes of transient synchrony. This is true even for bursts consisting of two spikes. In contrast, our simulations reveal that identical singlet-spiking neurons do not exhibit such complexity in the network. These results suggest a role for bursting dynamics in realizing itinerant complexity in neural circuits.

Probability Distributions of Means of IA and IF for Gaussian Noise and Its Application to an Anomaly Detection

The Hilbert–Huang transform (HHT) extracts the intrinsic oscillation modes of input data, and estimates instantaneous amplitude (IA) and frequency (IF) for each mode. The HHT is applied to detection of some anomaly structures of signals as well as to analysis of signals. However, only qualitative discussions have been conducted on the applications to the detections. To make more statistically-based arguments on the application of the HHT, we investigated the probability distribution of the means of IA and IF for white Gaussian noise and found that it fits the Pearson distribution rather than the normal distribution. We defined a feature value for an anomaly detection by using the probability density function estimated on the basis of the Pearson distribution. Our method does not require different models for different lengths of the segment over which the mean is calculated, and therefore it is useful especially for the case that the length cannot be fixed.

Detection of transient synchrony across oscillating receptors by the central electrosensory system of mormyrid fish

Recently, we reported evidence for a novel mechanism of peripheral sensory coding based on oscillatory synchrony. Spontaneously oscillating electroreceptors in weakly electric fish (Mormyridae) respond to electrosensory stimuli with a phase reset that results in transient synchrony across the receptor population (<xref ref-type="bibr" rid="bib5">Baker et al., 2015</xref>). Here, we asked whether the central electrosensory system actually detects the occurrence of synchronous oscillations among receptors. We found that electrosensory stimulation elicited evoked potentials in the midbrain exterolateral nucleus at a short latency following receptor synchronization. Frequency tuning in the midbrain resembled peripheral frequency tuning, which matches the intrinsic oscillation frequencies of the receptors. These frequencies are lower than those in individual conspecific signals, and instead match those found in collective signals produced by groups of conspecifics. Our results provide further support for a novel mechanism for sensory coding based on the detection of oscillatory synchrony among peripheral receptors.