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In the retina, the firing behaviors that ganglion cells exhibit when exposed to light stimuli are very important due to the significant roles they play in encoding the visual information. However, the detailed mechanisms, especially the intrinsic properties that generate and modulate these firing behaviors is not completely clear yet. In this study, 2 typical firing behaviors—i.e., tonic and phasic activities, which are widely observed in retinal ganglion cells (RGCs)—are investigated. A modified computational model was developed to explore the possible ionic mechanisms that underlie the generation of these 2 firing patterns. Computational results indicate that the generation of tonic and phasic activities may be attributed to the collective actions of 2 kinds of adaptation currents, i.e., an inactivating sodium current and a delayed-rectifier potassium current. The concentration of magnesium ions has crucial but differential effects in the modulation of tonic and phasic firings, when the model neuron is driven by N-methyl-D-aspartate (NMDA) -type synaptic input instead of constant current injections. The proposed model has robust features that account for the ionic mechanisms underlying the tonic and phasic firing behaviors, and it may also be used as a good candidate for modeling some other firing patterns in RGCs.

As the sole output neurons in the retina, ganglion cells play significant roles in transforming visual information into spike trains, and then transmitting them to the higher visual centers. However, coding strategies that retinal ganglion cells (RGCs) adopt to accomplish these processes are not completely clear yet. To clarify these issues, we investigate the coding properties of three types of RGCs (repetitive spiking, tonic firing, and phasic firing) by two different measures (spike-rate and spike-latency). Model results show that for periodic stimuli, repetitive spiking RGC and tonic RGC exhibit similar spike-rate patterns. Their spike- rates decrease gradually with increased stimulus frequency, moreover, variation of stimulus amplitude would change the two RGCs' spike-rate patterns. For phasic RGC, it activates strongly at medium levels of frequency when the stimulus amplitude is low. While if high stimulus amplitude is applied, phasic RGC switches to respond strongly at low frequencies. These results suggest that stimulus amplitude is a prominent factor in regulating RGCs in encoding periodic signals. Similar conclusions can be drawn when analyzes spike-latency patterns of the three RGCs. More importantly, the above phenomena can be accurately reproduced by Hodgkin's three classes of neurons, indicating that RGCs can perform the typical three classes of firing dynamics, depending on the distinctions of ion channel densities. Consequently, model results from the three RGCs may be not specific, but can also applicable to neurons in other brain regions which exhibit part(s) or all of the Hodgkin's three excitabilities.

P2P network is more suitable for distributed data flow processing. This paper concentrates on how to answer continuous join query in structured p2p overlay networks. In the algorithm proposed, the data that cannot contribute the queries’ results will not be distributed in the network based on global query indices. Experiment shows that the algorithm ensures the availability of join query and network traffic is reduced.

Event-related potential (ERP) studies report that early components P120, N170 and VPP are associated with face processing. Several lines of evidence suggest that VPP is the positive counterpart of N170, and they are generated by the same brain sources. However, whether P120 has a negative counterpart and the relations among these early components (i.e. P120, N170, VPP) remain unclear. In this study, the scalp electroencephalogram was recorded when the subjects passively viewed different stimuli, and ERP was calculated. The synchronization of electroencephalography signals between fronto-central and bilateral occipitotemporal sites was evaluated, and independent component analysis was employed to seek the face-sensitive independent components and their corresponding sources. We found that P120 had the negative counterpart, i.e., VN120; moreover, VN120-VPP and P120-N170 complexes were generated by the same sources located in fusiform gyrus, which reflected the same sequential neural activities of face processing.

A typical feature of neurons is their ability to encode neural information dynamically through spike frequency adaptation (SFA). Previous studies of SFA on neuronal synchronization were mainly concentrated on the correlated firing between neuron pairs, while the synchronization of neuron populations in the presence of SFA is still unclear. In this study, the influence of SFA on the population synchronization of neurons was numerically explored in electrically coupled networks, with regular, small-world, and random connectivity, respectively. The simulation results indicate that cross-correlation indices decrease significantly when the neurons have adaptation compared with those of nonadapting neurons, similar to previous experimental observations. However, the synchronous activity of population neurons exhibits a rather complex adaptation-dependent manner. Specifically, synchronization strength of neuron populations changes nonmonotonically, depending on the degree of adaptation. In addition, single neurons in the networks can switch from regular spiking to bursting with the increase of adaptation degree. Furthermore, the connection probability among neurons exhibits significant influence on the population synchronous activity, but has little effect on the burst generation of single neurons. Accordingly, the results may suggest that synchronous activity and burst firing of population neurons are both adaptation-dependent.

Effects of spike frequency adaptation (SFA) on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode: compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong-degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations.

The capability of a biological neuron to discriminate the intensity of external stimulus is measured in its dynamic range. In previous studies, a few factors have been reported to be able to enhance the dynamic range, e.g., electrical coupling and active dendrites. Here we numerically show that intrinsic channel noise within neurons has a subtle effect in neuronal dynamic range modulation. Our simulation results indicate that for relatively weak noise intensity, the dynamic range of the neuron is enhanced significantly. However, as the noise intensity becomes stronger, the dynamic range of the neuron is weakened. Further investigation suggests that sodium channel noise and potassium channel noise play opposite roles in modulating the dynamic range. Consequently, the model results suggest a new function of channel noise, that is, a proper value of noise intensity could optimize the dynamic range of neurons.