We spend one third of our life in sleep. The interesting point about the sleep is that the neurons are not quiescent during sleeping and they show synchronous oscillations at different regions. Especially sharp wave ripples are observed in the hippocampus. Here, we propose a simple phenomenological neural mass model for the CA1-CA3 network of the hippocampus considering the spike frequency adaptation for excitatory neurons. The model consists of one group of identical CA1 excitatory neurons, one group of identical CA1 inhibitory neurons, one group of identical CA3 excitatory neurons, and one group of identical CA3 inhibitory neurons. All the recurrent connections between the neurons of CA3 network are considered. For CA1 neurons the excitatory to inhibitory, inhibitory to excitatory and inhibitory to inhibitory connections are considered. CA1 and CA3 neurons are connected by long-range connections from CA3 excitatory neurons to both CA1 excitatory and inhibitory neurons. We show that this simple model can spontaneously generate the oscillations similar to the sharp waves in the CA3 network. The duration of the sharp waves is determined by the slow dynamic of the adaptation process. The excitatory inputs from CA3 network to the CA1 network during these sharp waves induce ripples in the CA1 network due to the interaction of excitatory and inhibitory neurons. We next show that contrary to intuition and in a very good agreement with the recent experimental findings, reduction of the excitation increases the amplitude of the ripples while decreases the frequency of them. This model can also spontaneously generate ripple doublets. The decrease in the excitation is associated with the increase in the probability of observing ripple doublets. Our results shed light on our understanding of the mechanism underlying the generation of sharp wave ripples.

Introduction: Chronic morphine exposure can cause addiction and affect synaptic plasticity, but the underlying neural mechanisms of this phenomenon remain unknown. Herein we used electrophysiologic approaches in hippocampal CA1 area to examine the effect of chronic morphine administration on short-term plasticity. Methods: Experiments were carried out on hippocampal slices taken from either control animals or animals made dependent via oral chronic morphine administration. Population spikes (PSs) were recorded from stratum pyramidale of CA1 following stimulation the Schaffer collateral afferents. For examining the short-term synaptic plasticity, paired pulse stimulations with inter pulse interval (IPI) of 10, 20, 80, and 200 ms were applied and paired pulse index (PPI) was calculated.Results: Chronic morphine exposure had no effect on the baseline response. A significant increase in PPI was observed in dependent slices at 80 ms IPI as compared to the control ones. There was no significant difference in baseline response between control and dependent slices when we used long term morphine, naloxone, and both. However, long term morphine administration caused significant difference in PPI at IPI of 20 ms. This effect was eliminated in the presence of naloxone.Conclusion: These findings suggest that morphine dependence could affect short-term plasticity in hippocampal CA1 area and increase the hippocampus network excitability.

In this paper, a new histogram-based method is introduced to make object detectors resistant to hostile attacks. In the following, this method was applied to two object detector models, YOLOV5 and FRCNN, and in this way, two models resistant to attacks were introduced. In order to verify the performance of the mentioned models, we performed the adversarial training process of these models with three targeted attacks TOG-vanishing, TOG-mislabeling, and TOG-fabrication and one untargeted attack, DAG. We have checked the efficiency of the introduced models on two data sets MSCOCO and PASCAL VOC, which are among the most famous data sets in the field of object recognition. The results show that this method, in addition to improving the adversarial accuracy, also improves the clean accuracy of the object detector models to some extent. The average clean accuracy of the YOLOv5-n model for the PASCAL VOC dataset, if adversarial attacks are applied to it, in the case where no defense method is applied, is 85.5%, and in the case where the histogram method is applied, the average accuracy is equal to with 87.36%. In the YOLOv5-n model, according to the results, the best adversarial accuracy of this model, which has increased compared to other models, is in TOG-vanishing and TOG-fabrication attacks, which are 48% and 52.36%, respectively.

In general, humans are very complex organisms, and therefore, research on their various dimensions and aspects including personality has become an attractive subject of research works. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to the humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of the individuals, first, a categorization of the related works is proposed, and then a hybrid framework based on the fuzzy neural networks (FNN) and the deep neural networks (DNN) is proposed, which improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The suggested approach uses the structural features of a social networks analysis (SNA) along with a linguistic (LA) analysis feature extracted from the description of the activities of the individuals and comparison with the previous similar research works. The results obtained well-illustrate the performance improvement of the proposed framework up to 83. 2% of the average accuracy of the personality dataset.