Related Experiment Video
Updated: Aug 10, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
Published on: March 18, 2019
Using Features for the Storage of Patterns in a Fully Connected Net
1Centre for Neural Networks, Kings College, London, UK
Abstract:
One of the many possible conditions for pattern storage in a Hopfield net is to demand that the local field vector be a pattern reconstruction. We use this criterion to derive a set of weights for the storage of correlated biased patterns in a fully connected net. The connections are built from the eigenvectors or principal components of the pattern correlation matrix. Since these are often identified with the features of a pattern set we have named this particular set of weights as the feature matrix. We present simulation results that show the feature matrix to be capable of storing up to N random patterns in a network of N spins. Basins of attraction are also investigated via simulation and we compare them with both our theoretical analysis and those of the pseudo-inverse rule. A statistical mechanical investigation using the replica trick confirms the result for storage capacity. Finally we discuss a biologicaly plausible learning rule capable of realising the feature matrix in a fully connected net. Copyright 1996 Elsevier Science Ltd
Related Concept Videos
Fixed Action Patterns
Neural Circuits
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Circuit Terminology
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
Network Function of a Circuit
Sequence Networks of Rotating Machines
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Storage