87 citations · 342 across the 15 of their papers we have counts for
16 papers
Efficiency and robustness partition the solution space for memory in recurrent networks
William Qian, Cengiz Pehlevan
In computational neuroscience, task-trained recurrent neural networks (RNNs) are commonly used as a testbed for exploring the space of recurrent circuit solutions compatible with a…
Discovering alternative solutions beyond the simplicity bias in recurrent neural networks
William Qian, Cengiz Pehlevan
Training recurrent neural networks (RNNs) to perform neuroscience-style tasks has become a popular way to generate hypotheses for how neural circuits in the brain might perform com…
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii
Early sensory systems in the brain rapidly adapt to fluctuating input statistics, which requires recurrent communication between neurons. Mechanistically, such recurrent communicat…
Biologically plausible single-layer networks for nonnegative independent component analysis
David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii
An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the…
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…
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?
Cengiz Pehlevan, Anirvan Sengupta, Dmitri B. Chklovskii
Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet, derivations of single-lay…
Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?
Reza Abbasi-Asl, Cengiz Pehlevan, Bin Yu +1
Several theories of early sensory processing suggest that it whitens sensory stimuli. Here, we test three key predictions of the whitening theory using recordings from 152 ganglion…
Self-calibrating Neural Networks for Dimensionality Reduction
Yuansi Chen, Cengiz Pehlevan, Dmitri B. Chklovskii
Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In t…
Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening
Cengiz Pehlevan, Dmitri B. Chklovskii
In analyzing information streamed by sensory organs, our brains face challenges similar to those solved in statistical signal processing. This suggests that biologically plausible…
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
Cengiz Pehlevan, Dmitri B. Chklovskii
To make sense of the world our brains must analyze high-dimensional datasets streamed by our sensory organs. Because such analysis begins with dimensionality reduction, modelling e…
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…
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization
Tao Hu, Cengiz Pehlevan, Dmitri B. Chklovskii
Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modeled by sparse dictionary learning. By minimizing the regularized r…
A Neuron as a Signal Processing Device
Tao Hu, Zaid J. Towfic, Cengiz Pehlevan +2
A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understo…