Modi cations to Recursive Auto-Associative Memory are presented, which allow it to store deeper and more complex data structures than previously reported. These modi cations include adding extra layers to the compressor and reconstructor networks, employing integer rather than real-valued representations, pre-conditioning the weights and pre-setting the representations to be compatible with the...

We present modifications to Recursive Auto-Associative Memory which increase its robustness and storage capacity. This is done by introducing an extra layer to the compressor and reconstructor networks, employing integer rather than realvalued representations, pre-conditioning the weights and presetting the representations to be compatible with them, and using a quick-prop modification. Initial...

Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent studies introduced fast weights only to regular RNNs, it is unknown whether fast weight memory is beneficial to gated RNNs. In this work, we report a significant synergy between long short-term memory (LSTM) networks...

Recursive Auto-Associative Memory (RAAM) structures show promise as a general representation vehicle that uses distributed patterns. However training is often difficult, which explains, at least in part, why only relatively small networks have been studied. We show a technique for transforming any collection of hierarchical structures into a set of training patterns for a sequential RAAM which ...

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An au...

Most models of neural associative memory have used networks with broad connectivity. However, this seems unrealistic from a neuroanatomical perspective. A simple model of associative memory with emergent properties was introduced by Hopfield [5]. We choose this widely known model to investigate the impact of connectivity on the storage capacity and the retrieval dynamics in artificial associati...

In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search...

The recent trends in optimisation of sustainability of production processes requires, amongst all the activities, a continuous detection and correction of process behaviours, monitoring those parameters critical to performance. Detection of special causes of variations is a basic task in manufacturing, that has to be performed continuously to maintain any process stable as well as predictable. ...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the pe...