Encoding strategy for maximum noise tolerance bidirectional associative memory

Reduced order multiport parallel and multidirectional neural associative memories

This paper proposes multiport parallel and multidirectional intraconnected associative memories of outer product type with reduced interconnections. Some new reduced order memory architectures such as k-directional and k-port parallel memories are suggested. These architectures are, also, very suitable for implementation of spatio-temporal sequences and multiassociative memories. It is shown that in the proposed memory architectures, a substational reduction in interconnections is achieved if the actual length of original N-bit long vectors is subdivided into k sublengths. Using these sublengths, submemory matrices, T ( s ) or W ( s ), are computed, which are then intraconnected to form k-port parallel or k-directional memories. The subdivisions of N-bit long vectors into k sublengths save ((k-1) x 100) / k % of interconnections. It is shown, by means of an example, that more than 80% reduction in interconnections is achieved. Minimum limit in bits on k as well as maximum limit on subdivisions in k is determined. The topologies of reduced interconnectivity developed in this paper are symmetric in structure and can be used to scale up to larger systems. The underlying principal of construction, storage and retrieval processes of such associative memories has been analyzed. The effect of complexity of different levels of reduced interconnectivity on the quality of retrieval, signal to noise ratio, and storage capacity has been investigated. The model possesses analogies to biological neural structures and digital parallel port memories commonly used in parallel and multiprocessing systems.

Associative learning in hierarchical self-organizing learning arrays

In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given.

A bidirectional heteroassociative memory for binary and grey-level patterns

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.