Topic: three-factor Hebbian learning

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