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The lesions induced by Ibotenic acid (IA) emulate some of the symptoms associated with schizophrenia, such as impaired working memory that is predominantly organized by the medial prefrontal cortex (mPFC), or difficulties in social interactions that aremainly organized by the amygdala (AMG). The plastic capacity of dendritic spines in neurons of the mPFC and AMG is modulated by molecules that participate in the known deterioration of working memory, although the influence of these on the socialization of schizophrenic patients is unknown. Here, the effect of a neonatal IA induced lesion on social behavior and working memory was evaluated in adult rats, along with the changes in cytoarchitecture of dendritic spines and their protein content, specifically the postsynaptic density protein 95 (PSD-95), Synaptophysin (Syn), AMPA receptors, and brain-derived neurotrophic factor (BDNF). Both working memory and social behavior were impaired, and the density of the spines, as well as their PSD-95, Syn, AMPA receptor and BDNF content was lower in IA lesioned animals. The proportional density of thin, mushroom, stubby and wide spines resulted in plastic changes that suggest the activation of compensatory processes in the face of the adverse effects of the lesion. In addition, the reduction in the levels of the modulating factors also suggests that the signaling pathways in which such factors are implicated would be altered in the brains of patients with schizophrenia. Accordingly, the experimental study of such signaling pathways is likely to aid the development of more effective pharmacological strategies for the treatment of schizophrenia.Public health travel restrictions (PHTR) are crucial measures during communicable disease outbreaks to prevent transmission during commercial airline travel and mitigate cross-border importation and spread. We evaluated PHTR implementation for US citizens on the Diamond Princess during its coronavirus disease (COVID-19) outbreak in Japan in February 2020 to explore how PHTR reduced importation of COVID-19 to the United States during the early phase of disease containment. link= Androgen Receptor Antagonist in vitro Using PHTR required substantial collaboration among the US Centers for Disease Control and Prevention, other US government agencies, the cruise line, and public health authorities in Japan. Original US PHTR removal criteria were modified to reflect international testing protocols and enable removal of PHTR for persons who recovered from illness. The impact of PHTR on epidemic trajectory depends on the risk for transmission during travel and geographic spread of disease. Lessons learned from the Diamond Princess outbreak provide critical information for future PHTR use.The Faroe Islands was one of the first countries in the Western Hemisphere to eliminate coronavirus disease (COVID-19). During the first epidemic wave in the country, 187 cases were reported between March 3 and April 22, 2020. Large-scale testing and thorough contact tracing were implemented early on, along with lockdown measures. Transmission chains were mapped through patient history and knowledge of contact with prior cases. The most common reported COVID-19 symptoms were fever, headache, and cough, but 11.2% of cases were asymptomatic. Among 187 cases, 8 patients were admitted to hospitals but none were admitted to intensive care units and no deaths occurred. Superspreading was evident during the epidemic because most secondary cases were attributed to just 3 infectors. Even with the high incidence rate in early March, the Faroe Islands successfully eliminated the first wave of COVID-19 through the early use of contact tracing, quarantine, social distancing, and large-scale testing.Human papillomavirus (HPV) infections are found in children, but transmission modes and outcomes are incompletely understood. link2 We evaluated oral samples from 331 children in Finland who participated in the Finnish Family HPV Study from birth during 9 follow-up visits (mean time 51.9 months). We tested samples for 24 HPV genotypes. Oral HPV prevalence for children varied from 8.7% (at a 36-month visit) to 22.8% (at birth), and 18 HPV genotypes were identified. HPV16 was the most prevalent type to persist, followed by HPV18, HPV33, and HPV6. Persistent, oral, high-risk HPV infection for children was associated with oral HPV carriage of the mother at birth and seroconversion of the mother to high-risk HPV during follow-up (odds ratio 1.60-1.92, 95% CI 1.02-2.74). Children acquire their first oral HPV infection at an early age. The HPV status of the mother has a major impact on the outcome of oral HPV persistence for her offspring.Neuronal networks in rodent primary visual cortex (V1) can generate oscillations in different frequency bands depending on the network state and the level of visual stimulation. High-frequency gamma rhythms, for example, dominate the network's spontaneous activity in adult mice but are attenuated upon visual stimulation, during which the network switches to the beta band instead. The spontaneous local field potential (LFP) of juvenile mouse V1, however, mainly contains beta rhythms and presenting a stimulus does not elicit drastic changes in network oscillations. We study, in a spiking neuron network model, the mechanism in adult mice allowing for flexible switches between multiple frequency bands and contrast this to the network structure in juvenile mice that lack this flexibility. The model comprises excitatory pyramidal cells (PCs) and two types of interneurons the parvalbumin-expressing (PV) and the somatostatinexpressing (SOM) interneuron. In accordance with experimental findings, the pyramidal-PV and pyramidal-SOM cell subnetworks are associated with gamma and beta oscillations, respectively. Androgen Receptor Antagonist in vitro Androgen Receptor Antagonist in vitro In our model, they are both generated via a pyramidal-interneuron gamma (PING) mechanism, wherein the PCs drive the oscillations. Furthermore, we demonstrate that large but not small visual stimulation activates SOM cells, which shift the frequency of resting-state gamma oscillations produced by the pyramidal-PV cell subnetwork so that beta rhythms emerge. Finally, we show that this behavior is obtained for only a subset of PV and SOM interneuron projection strengths, indicating that their influence on the PCs should be balanced so that they can compete for oscillatory control of the PCs. In sum, we propose a mechanism by which visual beta rhythms can emerge from spontaneous gamma oscillations in a network model of the mouse V1; for this mechanism to reproduce V1 dynamics in adult mice, balance between the effective strengths of PV and SOM cells is required.This work addresses the problem of network pruning and proposes a novel joint training method based on a multiobjective optimization model. Most of the state-of-the-art pruning methods rely on user experience for selecting the sparsity ratio of the weight matrices or tensors, and thus suffer from severe performance reduction with inappropriate user-defined parameters. Moreover, networks might be inferior due to the inefficient connecting architecture search, especially when it is highly sparse. link2 It is revealed in this work that the network model might maintain sparse characteristic in the early stage of the backpropagation (BP) training process, and evolutionary computation-based algorithms can accurately discover the connecting architecture with satisfying network performance. In particular, we establish a multiobjective sparse model for network pruning and propose an efficient approach that combines BP training and two modified multiobjective evolutionary algorithms (MOEAs). The BP algorithm converges quickly, and the two MOEAs can search for the optimal sparse structure and refine the weights, respectively. Experiments are also included to prove the benefits of the proposed algorithm. We show that the proposed method can obtain a desired Pareto front (PF), leading to a better pruning result comparing to the state-of-the-art methods, especially when the network structure is highly sparse.Brains process information in spiking neural networks. link3 Their intricate connections shape the diverse functions these networks perform. Yet how network connectivity relates to function is poorly understood, and the functional capabilities of models of spiking networks are still rudimentary. The lack of both theoretical insight and practical algorithms to find the necessary connectivity poses a major impediment to both studying information processing in the brain and building efficient neuromorphic hardware systems. The training algorithms that solve this problem for artificial neural networks typically rely on gradient descent. But doing so in spiking networks has remained challenging due to the nondifferentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients affect learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative's scale can substantially affect learning performance. When we combine surrogate gradients with suitable activity regularization techniques, spiking networks perform robust information processing at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.We study the learning dynamics and the representations emerging in recurrent neural networks (RNNs) trained to integrate one or multiple temporal signals. Combining analytical and numerical investigations, we characterize the conditions under which an RNN with n neurons learns to integrate D ( ≪ n ) scalar signals of arbitrary duration. We show, for linear, ReLU, and sigmoidal neurons, that the internal state lives close to a D -dimensional manifold, whose shape is related to the activation function. Each neuron therefore carries, to various degrees, information about the value of all integrals. link3 We discuss the deep analogy between our results and the concept of mixed selectivity forged by computational neuroscientists to interpret cortical recordings.Spatial Monte Carlo integration (SMCI) is an extension of standard Monte Carlo integration and can approximate expectations on Markov random fields with high accuracy. SMCI was applied to pairwise Boltzmann machine (PBM) learning, achieving superior results over those of some existing methods. The approximation level of SMCI can be altered, and it was proved that a higher-order approximation of SMCI is statistically more accurate than a lower-order approximation. However, SMCI as proposed in previous studies suffers from a limitation that prevents the application of a higher-order method to dense systems. This study makes two contributions. First, a generalization of SMCI (called generalized SMCI (GSMCI)) is proposed, which allows a relaxation of the above-mentioned limitation; moreover, a statistical accuracy bound of GSMCI is proved. Second, a new PBM learning method based on SMCI is proposed, which is obtained by combining SMCI and persistent contrastive divergence. The proposed learning method significantly improves learning accuracy.
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