A neuron as a signal processing device. - Tao Hu 005; Zaid J. Towfic; Cengiz Pehlevan; Alex V. Genkin; Dmitri B. Chklovskii (ACSSC, 2013)

A Hebbian/Anti-Hebbian network for online sparse dictionary learning derived from symmetric matrix factorization. - Tao Hu 005; Cengiz Pehlevan; Dmitri B. Chklovskii (ACSSC, 2014)

A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features. - Cengiz Pehlevan; Dmitri B. Chklovskii (ACSSC, 2014)

Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening. - Cengiz Pehlevan; Dmitri B. Chklovskii (Allerton, 2015)

A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks. - Cengiz Pehlevan; Dmitri B. Chklovskii (NIPS, 2015)

A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data. - Cengiz Pehlevan; Tao Hu 005; Dmitri B. Chklovskii (Neural Comput., 2015)

Do retinal ganglion cells project natural scenes to their principal subspace and whiten them? - Reza Abbasi-Asl; Cengiz Pehlevan; Bin Yu 001; Dmitri B. Chklovskii (ACSSC, 2016)

Self-calibrating neural networks for dimensionality reduction. - Yuansi Chen; Cengiz Pehlevan; Dmitri B. Chklovskii (ACSSC, 2016)

A clustering neural network model of insect olfaction. - Cengiz Pehlevan; Alexander Genkin; Dmitri B. Chklovskii (ACSSC, 2017)

Resource-efficient perceptron has sparse synaptic weight distribution. - Cengiz Pehlevan; Anirvan M. Sengupta (SIU, 2017)

Adversarial synapses: Hebbian/anti-Hebbian learning optimizes min-max objectives. - Cengiz Pehlevan; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2017)

Blind Nonnegative Source Separation Using Biological Neural Networks. - Cengiz Pehlevan; Sreyas Mohan; Dmitri B. Chklovskii (Neural Comput., 2017)

Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics. - Victor Minden; Cengiz Pehlevan; Dmitri B. Chklovskii (ACSSC, 2018)

Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching. - Andrea Giovannucci; Victor Minden; Cengiz Pehlevan; Dmitri B. Chklovskii (IEEE BigData, 2018)

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. - Anirvan M. Sengupta; Cengiz Pehlevan; Mariano Tepper; Alexander Genkin; Dmitri B. Chklovskii (NeurIPS, 2018)

Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? - Cengiz Pehlevan; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2018)

A Closer Look at Disentangling in β-VAE. - Harshvardhan Sikka; Weishun Zhong; Jun Yin 002; Cengiz Pehlevan (ACSSC, 2019)

A Spiking Neural Network with Local Learning Rules Derived from Nonnegative Similarity Matching. - Cengiz Pehlevan (ICASSP, 2019)

Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. - Dina Obeid; Hugo Ramambason; Cengiz Pehlevan (NeurIPS, 2019)

Neuroscience-inspired online unsupervised learning algorithms. - Cengiz Pehlevan; Dmitri B. Chklovskii (CoRR, 2019)

Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial neural networks. - Cengiz Pehlevan; Dmitri B. Chklovskii (IEEE Signal Process. Mag., 2019)

Blind Bounded Source Separation Using Neural Networks with Local Learning Rules. - Alper T. Erdogan; Cengiz Pehlevan (ICASSP, 2020)

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. - Blake Bordelon; Abdulkadir Canatar; Cengiz Pehlevan (ICML, 2020)

Associative Memory in Iterated Overparameterized Sigmoid Autoencoders. - Yibo Jiang; Cengiz Pehlevan (ICML, 2020)

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons. - Qianyi Li; Cengiz Pehlevan (NeurIPS, 2020)

Supervised Deep Similarity Matching. - Shanshan Qin; Nayantara Mudur; Cengiz Pehlevan (CoRR, 2020)

Statistical Mechanics of Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (CoRR, 2020)

Activation function dependence of the storage capacity of treelike neural networks. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2020)

Neurons as Canonical Correlation Analyzers. - Cengiz Pehlevan; Xinyuan Zhao; Anirvan M. Sengupta; Dmitri B. Chklovskii (Frontiers Comput. Neurosci., 2020)

Depth induces scale-averaging in overparameterized linear Bayesian neural networks. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (ACSCC, 2021)

Attention Approximates Sparse Distributed Memory. - Trenton Bricken; Cengiz Pehlevan (NeurIPS, 2021)

Out-of-Distribution Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2021)

Asymptotics of representation learning in finite Bayesian neural networks. - Jacob A. Zavatone-Veth; Abdulkadir Canatar; Benjamin S. Ruben; Cengiz Pehlevan (NeurIPS, 2021)

Exact marginal prior distributions of finite Bayesian neural networks. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (NeurIPS, 2021)

Exact priors of finite neural networks. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2021)

Asymptotics of representation learning in finite Bayesian neural networks. - Jacob A. Zavatone-Veth; Abdulkadir Canatar; Cengiz Pehlevan (CoRR, 2021)

Contrastive Similarity Matching for Supervised Learning. - Shanshan Qin; Nayantara Mudur; Cengiz Pehlevan (Neural Comput., 2021)

A Kernel Analysis of Feature Learning in Deep Neural Networks. - Abdulkadir Canatar; Cengiz Pehlevan (Allerton, 2022)

Neural Networks as Kernel Learners: The Silent Alignment Effect. - Alexander B. Atanasov; Blake Bordelon; Cengiz Pehlevan (ICLR, 2022)

Learning Curves for SGD on Structured Features. - Blake Bordelon; Cengiz Pehlevan (ICLR, 2022)

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? - Matthew Farrell; Blake Bordelon; Shubhendu Trivedi; Cengiz Pehlevan (ICLR, 2022)

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks. - Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2022)

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources. - Bariscan Bozkurt; Cengiz Pehlevan; Alper T. Erdogan (NeurIPS, 2022)

Natural gradient enables fast sampling in spiking neural networks. - Paul Masset; Jacob A. Zavatone-Veth; J. Patrick Connor; Venkatesh Murthy; Cengiz Pehlevan (NeurIPS, 2022)

Biologically plausible single-layer networks for nonnegative independent component analysis. - David Lipshutz; Cengiz Pehlevan; Dmitri B. Chklovskii (Biol. Cybern., 2022)

Contrasting random and learned features in deep Bayesian linear regression. - Jacob A. Zavatone-Veth; William L. Tong; Cengiz Pehlevan (CoRR, 2022)

On Neural Network Kernels and the Storage Capacity Problem. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (Neural Comput., 2022)

The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. - Alexander B. Atanasov; Blake Bordelon; Sabarish Sainathan; Cengiz Pehlevan (ICLR, 2023)

The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. - Blake Bordelon; Cengiz Pehlevan (ICLR, 2023)

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation. - Bariscan Bozkurt; Ates Isfendiyaroglu; Cengiz Pehlevan; Alper Tunga Erdogan (ICLR, 2023)

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation. - David Lipshutz; Cengiz Pehlevan; Dmitri B. Chklovskii (ICLR, 2023)

Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. - Nikhil Vyas 001; Alexander B. Atanasov; Blake Bordelon; Depen Morwani; Sabarish Sainathan; Cengiz Pehlevan (NeurIPS, 2023)

Loss Dynamics of Temporal Difference Reinforcement Learning. - Blake Bordelon; Paul Masset; Henry Kuo; Cengiz Pehlevan (NeurIPS, 2023)

Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks. - Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2023)

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry. - Bariscan Bozkurt; Cengiz Pehlevan; Alper T. Erdogan (NeurIPS, 2023)

Long Sequence Hopfield Memory. - Hamza Tahir Chaudhry; Jacob A. Zavatone-Veth; Dmitry Krotov; Cengiz Pehlevan (NeurIPS, 2023)

Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles. - Benjamin S. Ruben; Cengiz Pehlevan (NeurIPS, 2023)

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. - Jacob A. Zavatone-Veth; Paul Masset; William L. Tong; Joseph D. Zak; Venkatesh Murthy; Cengiz Pehlevan (NeurIPS, 2023)

Learning Curves for Deep Structured Gaussian Feature Models. - Jacob A. Zavatone-Veth; Cengiz Pehlevan (NeurIPS, 2023)

Neural networks learn to magnify areas near decision boundaries. - Jacob A. Zavatone-Veth; Sheng Yang 004; Julian A. Rubinfien; Cengiz Pehlevan (CoRR, 2023)

Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles. - Benjamin S. Ruben; Cengiz Pehlevan (CoRR, 2023)

Dynamics of Temporal Difference Reinforcement Learning. - Blake Bordelon; Paul Masset; Henry Kuo; Cengiz Pehlevan (CoRR, 2023)

Bandwidth Enables Generalization in Quantum Kernel Models. - Abdulkadir Canatar; Evan Peters; Cengiz Pehlevan; Stefan M. Wild; Ruslan Shaydulin (Trans. Mach. Learn. Res., 2023)

Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. - Blake Bordelon; Lorenzo Noci; Mufan Bill Li; Boris Hanin; Cengiz Pehlevan (ICLR, 2024)

Grokking as the transition from lazy to rich training dynamics. - Tanishq Kumar; Blake Bordelon; Samuel J. Gershman; Cengiz Pehlevan (ICLR, 2024)

A Dynamical Model of Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICML, 2024)

Infinite Limits of Multi-head Transformer Dynamics. - Blake Bordelon; Hamza Tahir Chaudhry; Cengiz Pehlevan (NeurIPS, 2024)

Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics. - William Qian 001; Jacob A. Zavatone-Veth; Benjamin S. Ruben; Cengiz Pehlevan (NeurIPS, 2024)

Scaling and renormalization in high-dimensional regression. - Alexander B. Atanasov; Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2024)

Asymptotic theory of in-context learning by linear attention. - Yue M. Lu; Mary I. Letey; Jacob A. Zavatone-Veth; Anindita Maiti; Cengiz Pehlevan (CoRR, 2024)

MLPs Learn In-Context. - William L. Tong; Cengiz Pehlevan (CoRR, 2024)

Spectral regularization for adversarially-robust representation learning. - Sheng Yang 004; Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2024)

Risk and cross validation in ridge regression with correlated samples. - Alexander B. Atanasov; Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2024)

A Brain-Inspired Regularizer for Adversarial Robustness. - Elie Attias; Cengiz Pehlevan; Dina Obeid (CoRR, 2024)

Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex. - Tanishq Kumar; Blake Bordelon; Cengiz Pehlevan; Venkatesh N. Murthy; Samuel J. Gershman (CoRR, 2024)

No Free Lunch From Random Feature Ensembles. - Benjamin S. Ruben; William L. Tong; Hamza Tahir Chaudhry; Cengiz Pehlevan (CoRR, 2024)

The Optimization Landscape of SGD Across the Feature Learning Strength. - Alexander B. Atanasov; Alexandru Meterez; James B. Simon; Cengiz Pehlevan (ICLR, 2025)

How Feature Learning Can Improve Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICLR, 2025)

Scaling Laws for Precision. - Tanishq Kumar; Zachary Ankner; Benjamin Frederick Spector; Blake Bordelon; Niklas Muennighoff; Mansheej Paul; Cengiz Pehlevan; Christopher Ré; Aditi Raghunathan (ICLR, 2025)

Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex. - Tanishq Kumar; Blake Bordelon; Cengiz Pehlevan; Venkatesh N. Murthy; Samuel J. Gershman (ICLR, 2025)

MLPs Learn In-Context on Regression and Classification Tasks. - William Lingxiao Tong; Cengiz Pehlevan (ICLR, 2025)

Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space. - Sheng Yang; Peihan Liu; Cengiz Pehlevan (Trans. Mach. Learn. Res., 2025)

A precise asymptotic analysis of learning diffusion models: theory and insights. - Hugo Cui; Cengiz Pehlevan; Yue M. Lu (CoRR, 2025)

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer. - Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models. - Alexander B. Atanasov; Blake Bordelon; Jacob A. Zavatone-Veth; Courtney Paquette; Cengiz Pehlevan (CoRR, 2025)

Adaptive kernel predictors from feature-learning infinite limits of neural networks. - Clarissa Lauditi; Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)

Learning richness modulates equality reasoning in neural networks. - William L. Tong; Cengiz Pehlevan (CoRR, 2025)

Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining. - Rosie Zhao; Alexandru Meterez; Sham M. Kakade; Cengiz Pehlevan; Samy Jelassi; Eran Malach (CoRR, 2025)

Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism. - Mete Erdogan; Cengiz Pehlevan; Alper T. Erdogan (CoRR, 2025)

Don't be lazy: CompleteP enables compute-efficient deep transformers. - Nolan Dey; Bin Claire Zhang; Lorenzo Noci; Mufan Bill Li; Blake Bordelon; Shane Bergsma; Cengiz Pehlevan; Boris Hanin; Joel Hestness (CoRR, 2025)

A Simplified Analysis of SGD for Linear Regression with Weight Averaging. - Alexandru Meterez; Depen Morwani; Costin-Andrei Oncescu; Jingfeng Wu; Cengiz Pehlevan; Sham M. Kakade (CoRR, 2025)

Transfer Learning in Infinite Width Feature Learning Networks. - Clarissa Lauditi; Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)

Discovering alternative solutions beyond the simplicity bias in recurrent neural networks. - William Qian 001; Cengiz Pehlevan (CoRR, 2025)

Pretrain-Test Task Alignment Governs Generalization in In-Context Learning. - Mary I. Letey; Jacob A. Zavatone-Veth; Yue M. Lu; Cengiz Pehlevan (CoRR, 2025)

Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time. - Blake Bordelon; Mary I. Letey; Cengiz Pehlevan (CoRR, 2025)

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling. - Alexandru Meterez; Depen Morwani; Jingfeng Wu; Costin-Andrei Oncescu; Cengiz Pehlevan; Sham M. Kakade (CoRR, 2025)