Topic: t-SNE

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

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared 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

Animals make decisions based on high-dimensional sensory inputs. Obtaining their low-dimensional disentangled representations, ideally in an unsupervised manner, is essential for straightforward downstream information processing. Although the machine learning field has developed useful nonlinear dimensionality reduction methods such as t-distributed 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