Model fitting

Build and evaluate correlative species-distribution and ecological-niche models with R. The course connects niche theory with occurrence-data quality, environmental predictors, model fitting and validation, then examines spatial and temporal projections and their limitations. Participants learn to recognize common modelling pitfalls, automate reproducible analyses and apply workflows to example or personal species datasets. 2027-02-01–2027-02-05, 15:00–18:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €480. Basic R knowledge is strongly recommended. Live classes are complemented

Computational modelling is a powerful, theoretically robust tool which allows scientists and clinicians to understand fine-grained changes in cognition from moment to moment. This offers a novel means to measure fluctuations in cognitive constructs relevant to mental health, enabling the design of theoretically precise interventions, a mechanistic understanding of existing interventions, and/or a means to enact cognitive training. However, barriers to entry are high: computational modelling is still a niche skill, and interpretation of model fitting estimates can be opaque without appropriate

Model fitting methods have been widely used in neuroscience to infer the parameters of a biophysical system from its responses to experimental stimulations. For instance, the parameters of a chemical synapse (e.g. the number of presynaptic vesicles, or its depression time constant) can be estimated from its postsynaptic responses to evoked stimuli. However, these estimates critically depend on the stimulation protocol being used. Experiments are often conducted with non-adaptive stimulation protocols that may not yield enough information about these parameters. Here, we propose using Bayesian