Models of Ocular Dominance Column Formation: Analytical and Computational Results. - Kenneth D. Miller; Joseph B. Keller; Michael P. Stryker (NIPS, 1988)

Network model of ocular dominance column formation: Analytical results. - Kenneth D. Miller; Joseph B. Keller; Michael P. Stryker (Neural Networks, 1988)

Network model of ocular dominance column formation: Computational results. - Kenneth D. Miller; Michael P. Stryker; Joseph B. Keller (Neural Networks, 1988)

Analysis of Linsker's Simulations of Hebbian Rules. - David J. C. MacKay; Kenneth D. Miller (Neural Comput., 1990)

Derivation of Linear Hebbian Equations from a Nonlinear Hebbian Model of Synaptic Plasticity. - Kenneth D. Miller (Neural Comput., 1990)

The Role of Constraints in Hebbian Learning. - Kenneth D. Miller; David J. C. MacKay (Neural Comput., 1994)

Development of spatiotemporal receptive fields of simple cells: II. Simulation and analysis. - Stefan Wimbauer; Oliver G. Wenisch; J. Leo van Hemmen; Kenneth D. Miller (Biol. Cybern., 1997)

Development of spatiotemporal receptive fields of simple cells: I. Model formulation. - Stefan Wimbauer; Oliver G. Wenisch; Kenneth D. Miller; J. Leo van Hemmen (Biol. Cybern., 1997)

Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cel. - Todd W. Troyer; Kenneth D. Miller (Neural Comput., 1997)

Equivalence of a Sprouting-And-Retraction Model and Correlation-Based Plasticity Models of Neural Development. - Kenneth D. Miller (Neural Comput., 1998)

Cross-channel correlations in tetrode recordings: implications for spike-sorting. - Sergei P. Rebrik; Brian D. Wright; Alfred A. Emondi; Kenneth D. Miller (Neurocomputing, 1999)

A model of visual cortical temporal frequency tuning. - Anton E. Krukowski; Todd W. Troyer; Kenneth D. Miller (Neurocomputing, 2001)

A model of cross-orientation inhibition in cat primary visual cortex. - Thomas Z. Lauritzen; Anton E. Krukowski; Kenneth D. Miller (Neurocomputing, 2001)

Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule. - Michael Eisele; Kenneth D. Miller (NIPS, 2002)

Local correlation-based ("push-pull") circuitry can account for non-linear summation of stimuli in a model of cat V1. - Thomas Z. Lauritzen; Kenneth D. Miller (Neurocomputing, 2002)

The contributions of inhibition and noise to responses in V1. - Thomas Z. Lauritzen; Kenneth D. Miller (Neurocomputing, 2004)

Maximally Reliable Markov Chains Under Energy Constraints. - G. Sean Escola; Michael Eisele; Kenneth D. Miller; Liam Paninski (Neural Comput., 2009)

Learning unbelievable probabilities. - Xaq Pitkow; Yashar Ahmadian; Kenneth D. Miller (NIPS, 2011)

Learning unbelievable marginal probabilities - Xaq Pitkow; Yashar Ahmadian; Kenneth D. Miller (CoRR, 2011)

Mathematical Equivalence of Two Common Forms of Firing Rate Models of Neural Networks. - Kenneth D. Miller; Francesco Fumarola (Neural Comput., 2012)

Analysis of the Stabilized Supralinear Network. - Yashar Ahmadian; Daniel B. Rubin; Kenneth D. Miller (Neural Comput., 2013)

Divisive Feature Normalization Improves Image Recognition Performance in AlexNet. - Michelle Miller 005; SueYeon Chung; Kenneth D. Miller (ICLR, 2022)

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations. - Caleb J. Holt; Kenneth D. Miller; Yashar Ahmadian (PLoS Comput. Biol., 2024)