Distinct hippocampal network states support theta phase precession and theta sequences in CA1

The hippocampus likely uses temporal coding to represent complex memories via mechanisms such as theta phase precession and theta sequences. Theta sequences are rapid sweeps of spikes from multiple place cells, encoding past or planned trajectories or non-spatial information. Phase precession, the correlation between a place cell's theta firing phase and animal position has been suggested to facilitate sequence emergence. We find that CA1 phase precession varies strongly across cells and environmental contingencies. Phase precession depends on the CA1 network state, and is only present when the medium gamma oscillation (60-90 Hz, linked to Entorhinal inputs) dominates. Conversely, theta sequences are most evident for non-precessing cells or with leading slow gamma (20-45 Hz, linked to CA3 inputs). These results challenge the view that phase precession is the mechanism underlying the emergence of theta sequences and point at a 'dual network states' model for hippocampal temporal code, potentially supporting merging of memory and exogenous information in CA1.

Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the Pandemic

The COVID-19 pandemic has become one of the biggest major health crises reported due its massive impact on many countries. From mental health experts, we know that we cannot lose sight of an equally alarming issue which is the long-term mental health impact the pandemic is going to leave on the society. The rapid spread of the pandemic gives little chance to prepare for or even process all that has happened in terms of job losses and the complete uprooting of everyday life and relationships. It is understandable that students may feel irritable, frustrated, or sad sometimes. Loneliness, confusion, and anxiety are also common, but the issue is how we can know if students’ emotions are a normal reaction to an abnormal situation. Therefore, online mental health education has become pretty important for students during the pandemic. Furthermore, it is important to evaluate the quality of online mental health education through microlessons. In this paper, based on Q-learning algorithm, the real-time adaptive bitrate (ABR) configuration parameters mechanism is proposed to detect the changes of network state constantly and select the optimal precalculated configuration according to the current network state. The simulation results show that the proposed algorithm based on Q-learning outperforms other baselines in average latency, average bitrate, and Mean Opinion Score (MOS) on Chrome DevTools and Clumsy. Meanwhile, the experimental results also reveal that the average number of identified mental health problems of the proposed mechanism has always been the best with the bandwidth from 10 Mbit/s to 500 Mbit/s.

Real-time monitoring of the prototype design of electric system by the ubidots platform

<span>In this paper, a prototype DC electric system was practically designed. The idea of the proposed system was derived from the microgrid concept. The system contained two houses each have a DC generator and load that consists of four 12 V DC lamps. Each house is controlled fully by Arduino UNO microcontroller to work in Island mode or connected it with the second house or main electric network. House operating mode depends on the power generated by its source and the availability of the main network. Under all operating cases, the minimum price of electricity consumption should satisfy as possible. Information between the houses about the operating mode and the main network state was exchanging wirelessly with the help of the RF-HC12. This information uploaded to the Ubidots platform by the Wi-Fi-ESP8266 included in the node MCU microcontroller. This platform has several advantages such as capture, visualization, analysis, and management of data. The system was examined for different cases to verify its working by varying the load in each building. All tested states showed that the houses transfer from one mode to another automatically with high reliability and minimum energy cost. The information about the main grid states and the sources of the houses were monitored and stored at the Ubidots platform.</span>

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.

Smart substation network quality monitoring and fault prediction

The smart substation communication network is the basis for information sharing of various devices in the substation. Its operation status has an important impact on the safe operation of the substation and even the power grid. Therefore, real-time status monitoring of the smart substation communication network is becoming more and more important. Aiming at the problems of single dimension, insufficient real-time performance and manual fault analysis in existing substation communication network state monitoring technology, this paper proposes a method of smart substation communication network state monitoring and fault prediction based on network communication quality. This paper uses switch ACL technology and coloring technology to obtain communication quality indexes such as bandwidth utilization, delay, and packet loss rate in real time; based on a multi-dimensional evaluation algorithm, a comprehensive evaluation model of network communication quality is constructed; the model of the relationship between abnormal network communication quality and failures is established. Finally, real-time monitoring of network communication quality and fault prediction are realized. The application analysis in a typical 110 kV substation shows that this method can effectively evaluate the network communication quality and accurately predict failures, and can guide operationer and maintenaner to quickly restore the normal operation of the communication network.