A graduate student from southeast Asia, now based in a European country and missing her homeland, agonizes over her next career move.

Graduate students are not machines. Behaving like one during your programme will leave you frustrated and unfulfilled, says Gauthier Weissbart.

We seek to understand if there is interest in being able to attend NeurIPS in Europe, i.e. without travelling to San Diego, US. In the following, assume that it is possible to present accepted papers ...

Pyramidal cells are classically thought to comprise the excitatory output of the subiculum. Here, the authors show the existence of “ovoid cells”, excitatory subiculum neurons with specialized gene ex...

The majority of cells in the cerebral cortex are unspecialized, according to an unpublished analysis—and scientists need to take care in naming neurons, the researchers warn.

Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable. Moreover, the dichotomy between multidimensional spaces and purportedly richer representational formats, such as graph representations, is misleading. We review what the different notions of dimension in cognitive science imply for how these latent representations should be used and interpreted.