Recurrent Autoassociative Networks and Holistic Computations

An Autoassociative Neural Network Model of Paired-Associate Learning

Hebbian heteroassociative learning is inherently asymmetric. Storing a forward association, from item A to item B, enables recall of B (given A), but does not permit recall of A (given B). Recurrent networks can solve this problem by associating A to B and B back to A. In these recurrent networks, the forward and backward associations can be differentially weighted to account for asymmetries in...

Holographic Recurrent Networks

Holographic Recurrent Networks (HRNs) are recurrent networks which incorporate associative memory techniques for storing sequential structure. HRNs can be easily and quickly trained using gradient descent techniques to generate sequences of discrete outputs and trajectories through continuous space. The performance of HRNs is found to be superior to that of ordinary recurrent networks on these ...

Recurrent Autoassociative Networks: Developing Distributed Representations of Hierarchically Structured Sequences by Autoassociation

Network Capacity for Latent Attractor Computation

Attractor networks have been one of the most successful paradigms in neural computation and have been used as models of computation in the nervous system Many experimentally observed phenomena such as coherent population codes contextual representations and replay of learned neural activity patterns are explained well by attractor dynamics Recently we proposed a paradigm called latent attractor...

High Order Neural Networks for Efficient Associative Memory Design

We propose learning rules for recurrent neural networks with high-order interactions between some or all neurons. The designed networks exhibit the desired associative memory function: perfect storage and retrieval of pieces of information and/or sequences of information of any complexity.