Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales

Technical debt and waste in non-functional requirements documentation:an exploratory study

Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as any other software artefact. Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation. Method: Based on a subset of data acquired from the most recent NaPiRE (Naming the Pain in Requirements Engineering) survey, we calculate, for a standard set of NFR types, how often respondents state they document a specific type of NFR when they also state that it is important. This allows us to quantify the occurrence of potential Technical Debt and Waste. Results: Based on 398 survey responses, four NFR types (Maintainability, Reliability, Usability, and Performance) are labelled as important but they are not documented by more than 22% of the respondents. We interpret that these NFR types have a higher risk of Technical Debt than other NFR types. Regarding Waste, 15% of the respondents state they document NFRs related to Security and they do not consider it important. Conclusions: There is a clear indication that there is a risk of Technical Debt for a fixed set of NFRs since there is a lack of documentation of important NFRs. The potential risk of incurring Waste is also present but to a lesser extent