Mechanistic models

…synthesis, kinetic measurements and mechanistic models, data-driven purification and AI-assisted R&D. It serves researchers, students and industrial chemists using automation in organic, medicinal and process chemistry. The 24 November programme has registration at 10:00, opening remarks at 10:30, scient

Build and evaluate short-term ecological forecasts by repeatedly linking observations, models and predictions. A biodiversity challenge uses shrub, rabbit and dung-beetle data from Doñana with remote-sensing measurements. Participants process time series and satellite indices, compare autoregressive, hierarchical, correlational and mechanistic models, propagate uncertainty and assess forecasts against benchmarks. Reproducible R workflows lead to a submitted forecast and discussion of how predictions support conservation and adaptive management. Live sessions: 25–29 January, 14:00–18:30 daily,

This funding opportunity supports transformative, mechanistic research aimed at understanding how the human brain functions. Applications should seek to reveal how the dynamic activity of individual neurons and ensembles of neurons within spatially organized brain networks gives rise to internal experiences, such as sensations, perceptions, emotions, thoughts, and memories, as well as to observable motor and social behaviors. To achieve fundamental mechanistic insights into human brain function, proposed studies are expected to leverage opportunities made possible by invasive surgical procedur

…Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to bu

…A mechanistic understanding will require models that align with experimental measurements and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Building such models has remained a central challenge. I will present our work on addressing this challenge. We have developed machine learning me

…Mechanistic models, especially spiking neural networks, are crucial for understanding how these interacting variables influence neural activity. However, the process of adjusting model parameters to match experimental recordings, i.e., inverse modeling, is challenging, and often requires a trade-off between model complexity and tractability. Consequently, models often use fixed or manually tuned parameters, limiting their dynamic range and explanatory power while making cross-study

…approach: comparing a suite of mechanistic models, directly fit to behavior, that embody competing hypotheses about single-cell or population neura

…This allows us to propose mechanistic models of emotional states.

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their pr