87 citations · 167 across the 3 of their papers we have counts for
5 papers
Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics
Victor Minden, Cengiz Pehlevan, Dmitri B. Chklovskii
Artificial neural networks that learn to perform Principal Component Analysis (PCA) and related tasks using strictly local learning rules have been previously derived based on the…
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching
Andrea Giovannucci, Victor Minden, Cengiz Pehlevan +1
Big data problems frequently require processing datasets in a streaming fashion, either because all data are available at once but collectively are larger than available memory or…
Blind nonnegative source separation using biological neural networks
Cengiz Pehlevan, Sreyas Mohan, Dmitri B. Chklovskii
Blind source separation, i.e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing. Here, we address a specia…
A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features
Cengiz Pehlevan, Dmitri B. Chklovskii
Despite our extensive knowledge of biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that sin…
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
Cengiz Pehlevan, Tao Hu, Dmitri B. Chklovskii
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal…