4.8 Article

Efficiency of Local Learning Rules in Threshold-Linear Associative Networks

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

Unsupervised learning by competing hidden units

Dmitry Krotov et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Mathematical & Computational Biology

Deep Learning With Asymmetric Connections and Hebbian Updates

Yali Amit

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2019)

Article Neurosciences

Inferring learning rules from distributions of firing rates in cortical neurons

Sukbin Lim et al.

NATURE NEUROSCIENCE (2015)

Review Neurosciences

The log-dynamic brain: how skewed distributions affect network operations

Gyoergy Buzsaki et al.

NATURE REVIEWS NEUROSCIENCE (2014)

Article Neurosciences

Specific evidence of low-dimensional continuous attractor dynamics in grid cells

KiJung Yoon et al.

NATURE NEUROSCIENCE (2013)

Article Biochemical Research Methods

Optimal Properties of Analog Perceptrons with Excitatory Weights

Claudia Clopath et al.

PLOS COMPUTATIONAL BIOLOGY (2013)

Article Biochemical Research Methods

A balanced memory network

Yasser Roudi et al.

PLOS COMPUTATIONAL BIOLOGY (2007)

Article Physics, Fluids & Plasmas

Localized activity profiles and storage capacity of rate-based autoassociative networks

Yasser Roudi et al.

PHYSICAL REVIEW E (2006)

Article Physics, Fluids & Plasmas

Disappearance of spurious states in analog associative memories

Y Roudi et al.

PHYSICAL REVIEW E (2003)