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…to segmentation and cleaning, collect landmarks and semilandmarks, and derive shape variables using Procrustes alignment and sliding procedures. Statistical exercises use principal components and regression to interpret size and shape variation; visualisations include deformations, morphings and heatmaps. Live sessions: 9–13 November, 13:00–18:00 daily, Europe/Madrid (CET, UTC+01:00). Total 30 hours: 25 live plus five independent.

Nov 9–13, 2026

Workshop · Computational Biology

Solve shape-analysis problems in R using measurements, landmarks and outlines. The programme develops data organisation and image processing before geometric transformations, alternative superimposition methods and Procrustes analysis. Participants visualise shape differences, test allometry and multivariate hypotheses, and compare principal-component, discriminant and regression approaches. Outline analysis introduces several Fourier representations and their statistical interpretation; participants can bring their own morphological datasets. Live sessions: 10, 11, 14, 15, 16, 17 and 18 Dece

Dec 10–18, 2026

Workshop · Statistics

A rate code for position error in a ring attractor model of path integration

Gorkem Secer, Ravikrishnan P. Jayakumar, Manu Madhav et al. · COSYNE 2023

…In the absence of external landmarks, path integration accumulates error, causing the internal representation of position to drift relative to the external world [1,2]. In addition to the well-established role of landmarks in correcting this error, recent experiments identified a new role of landmarks: recalibrating the path-integration gain, a factor mapping the animal’s movement through space to an updating of position in its internal representation [3]. Here, we set out to understand the mechanisms

ePoster · Computational Neuroscience

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framewo

Thu, Mar 9, 2023 · 15:00 UTC

Seminar · Neuroscience