t-SNE

For many animals, color vision is important for survival, contributing to critical behaviors such as identifying food, detecting predators, and recognizing mates. How colors are processed in the central nervous system of di- and trichromatic vertebrate species (2-3 color cones) has been a topic of considerable research over the last decades. In contrast, the neuronal circuits supporting color vision in tetrachromatic vertebrates (4 color cones), such as birds, remain poorly understood. Through a combination of display engineering, large-scale Neuropixels recordings, and computational methods,

…stochastic neighbor embedding (t-SNE) [1], their implementation in simple biological circuits remains unclear. Here, we develop a biologically plausible dimensionality reduction algorithm compatible with t-SNE. The proposed algorithm, named Hebbian

Dementia is characterised by a profound decline in cognitive and behavioural functions, drastically affecting daily life and social interactions. There is a pressing need for novel therapeutic strategies, particularly if these can be repurposed from available medicine – recent research has suggested antidiabetic drugs may have neuroprotective properties, but validation requires translatable pre-clinical models paired with unbiased assessment. Rocky Mountain Laboratory (RML) scrapie is a well-characterised model that mimics many of the hallmarks of neurodegeneration seen in dementia but does no

Temazepam is a widely used insomnia drug, which normalizes sleep architecture and enhances slow-wave sleep. Our study aimed to showcase two methods of explaining a sleep-EEG classification model, in the input and representation space. The goal was to evaluate the effectiveness of these explanations in studying the influence of temazepam on sleep architecture. We fine-tuned BErt-inspired Neural Data Representations (BENDR) encoder pretrained on the TUH EEG Corpus dataset to sleep stage classification. The obtained (sleepBENDR) model is explained using Class Activation Mapping (CAM), which indic

In this study, we aim to broaden our understanding of the mouse’s primary visual cortex by modeling data provided by the datasets from Stringer et al. (2019) and the Allen Institute. We investigated the roles of the different excitatory and inhibitory cell types in the visual cortex whose functions remain unclear. Using the Stringer dataset, we explored the correlation between the activity of excitatory cells in the mouse visual cortex and mouse running behavior without any visual input. Principle Component Analysis (PCA) was applied to reduce the dimension of our neuron activity data of 119

…to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneo