Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

The Role of OmpR in the Expression of Genes of the KdgR Regulon Involved in the Uptake and Depolymerization of Oligogalacturonides in Yersinia enterocolitica

Oligogalacturonide (OGA)-specific porins of the KdgM family have previously been identified and characterized in enterobacterial plant pathogens. We found that deletion of the gene encoding response regulator OmpR causes the porin KdgM2 to become one of the most abundant proteins in the outer membrane of the human enteropathogen Yersinia enterocolitica. Reporter gene fusion and real-time PCR analysis confirmed that the expression of kdgM2 is repressed by OmpR. We also found that kdgM2 expression is subject to negative regulation by KdgR, a specific repressor of genes involved in the uptake and metabolism of pectin derivatives in plant pathogens. The additive effect of kdgR and ompR mutations suggested that KdgR and OmpR regulate kdgM2 expression independently. We confirmed that kdgM2 occurs in an operon with the pelP gene, encoding the periplasmic pectate lyase PelP. A pectinolytic assay showed strong upregulation of PelP production/activity in a Y. enterocolitica strain lacking OmpR and KdgR, which corroborates the repression exerted by these regulators on kdgM2. In addition, our data showed that OmpR is responsible for up regulation of the kdgM1 gene encoding the second specific oligogalacturonide porin KdgM1. This indicates the involvement of OmpR in the reciprocal regulation of both KdgM1 and KdgM2. Moreover, we demonstrated the negative impact of OmpR on kdgR transcription, which might positively affect the expression of genes of the KdgR regulon. Binding of OmpR to the promoter regions of the kdgM2-pelP-sghX operon, and kdgM1 and kdgR genes was confirmed using the electrophoretic mobility shift assay, suggesting that OmpR can directly regulate their transcription. We also found that the overexpression of porin KdgM2 increases outer membrane permeability. Thus, OmpR-mediated regulation of the KdgM porins may contribute to the fitness of Y. enterocolitica in particular local environments

Five points to check when comparing visual perception in humans and machines

With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception. We demonstrate and apply these ideas through three case studies. The first case study shows how human bias can affect how we interpret results, and that several analytic tools can help to overcome this human reference point. In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks. Thereby, we show that contrary to previous suggestions, feedback mechanisms might not be necessary for the tasks in question. The third case study highlights the importance of aligning experimental conditions. We find that a previously-observed difference in object recognition does not hold when adapting the experiment to make conditions more equitable between humans and machines. In presenting a checklist for comparative studies of visual reasoning in humans and machines, we hope to highlight how to overcome potential pitfalls in design or inference.Comment: V3: minor changes like in published JOV version (https://doi.org/10.1167/jov.21.3.16) V2: New title; added general section (checklist); manuscript restructured such that each case study is one chapter; adversarial examples in first study replaced by different analysi