Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

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Antidepressant use or withdrawal can lead to side effects such as weight changes and sexual dysfunction. These effects, as well as depression and antidepressants themselves, are stigmatized topics. Reddit is a social media plat-form which encourages discussion of such stigmatized topics by providing semi-anonymity and therefore increasing self-disclosure. This study collects data from the subreddit r/depression and identifies potential antidepressant side effects, withdrawal effects, and symptoms of depression. Epistemic network analysis (ENA) is applied to the data set as an exploratory analysis. It is well-suited for this study because ENA focuses on relationships between themes in the text, and these relationships are key to untangling the potentially similar effects of depression, antidepressant use, and antidepressant withdrawal. The size and structure of this data set and others from social media pose both opportunities and challenges. This work provides an example of potential applications for tools such as ROCK, justifier and threaded ENA which are designed for use with social media data.

The mammalian cortex integrates and processes information to transform sensory inputs into perceptions and motor outputs. These operations are performed by networks of excitatory and inhibitory neurons distributed through the cortical layers. Parvalbumin interneurons (PVIs) are the most abundant type of inhibitory cortical neuron. With axons projecting within and between layers, PVIs supply feedforward and feedback inhibition to control and modulate circuit function. Distinct populations of excitatory neurons recruit different PVI populations, but the specializations of these synapses are poorly understood. Here, we targeted a genetically encoded hybrid voltage sensor to PVIs and used fluorescence imaging in mouse somatosensory cortex slices to record their voltage changes. Stimulating a single visually identified excitatory neuron with small-tipped theta-glass electrodes depolarized multiple PVIs, and a common threshold suggested that stimulation elicited unitary synaptic potentials in response to a single excitatory neuron. Excitatory neurons depolarized PVIs in multiple layers, with the most residing in the layer of the stimulated neuron. Spiny stellate cells depolarized PVIs more strongly than pyramidal cells by up to 77%, suggesting a greater role for stellate cells in recruiting PVI inhibition and controlling cortical computations. Response half-width also varied between different excitatory inputs. These results demonstrate functional differences between excitatory synapses on PVIs.

Epistemic network analysis (ENA) is a powerful tool for quantifying complex qualitative data. The visualization and analytical power of ENA could be extended by adding additional meaning to edges within networks including semantic relationships and the ability to connect more than two nodes. Here, we use Reddit data on social support for antidepressant side effects to highlight the ways in which ENA network edges could be extended with elements of semantic networks and hypergraphs. The types of support seen in discussions of anti-depressant use on social media are representations of multiple complex conceptual relationships between potential side effects of antidepressants, cessation/withdrawal from antidepressants, and requests and offers for social support. Together, the elements of semantic networks, hypergraphs, and ENA provide a richer picture of complex interactions such as the discussions of antidepressant side effects in Reddit threads.