Associative Content-addressable Memory Research Articles

Singular value Decomposition applied to Associative Memory of Hopfield Neural Network

Content-addressable memories are useful for storage and data retrieval from arrays of sensors. The Hopfield neural network is a model of associative content-addressable memory with a simple flexible structure. Design of this artificial neural network is capable of memorizing large quantity of data and recalling the same from available information. In this work, we have shown that with corrupted input dataset, the correct set of data can be retrieved from approximated or compressed associative memory matrix. The idea has been explained through an experiment using binary datasets. The proposed methodology will increase the storage capacity of associative memory. In the experiment we have shown that it will minimize the number of arithmetic operations involved in recovery process of the Hopfield model but will also ensure correctness of information retrieval from the synaptic matrix from a corrupted dataset as input.

Memory for events and their spatial context: models and experiments.

The computational role of the hippocampus in memory has been characterized as: (i) an index to disparate neocortical storage sites; (ii) a time-limited store supporting neocortical long-term memory; and (iii) a content-addressable associative memory. These ideas are reviewed and related to several general aspects of episodic memory, including the differences between episodic, recognition and semantic memory, and whether hippocampal lesions differentially affect recent or remote memories. Some outstanding questions remain, such as: what characterizes episodic retrieval as opposed to other forms of read-out from memory; what triggers the storage of an event memory; and what are the neural mechanisms involved? To address these questions a neural-level model of the medial temporal and parietal roles in retrieval of the spatial context of an event is presented. This model combines the idea that retrieval of the rich context of real-life events is a central characteristic of episodic memory, and the idea that medial temporal allocentric representations are used in long-term storage while parietal egocentric representations are used to imagine, manipulate and re-experience the products of retrieval. The model is consistent with the known neural representation of spatial information in the brain, and provides an explanation for the involvement of Papez's circuit in both the representation of heading direction and in the recollection of episodic information. Two experiments relating to the model are briefly described. A functional neuroimaging study of memory for the spatial context of life-like events in virtual reality provides support for the model's functional localization. A neuropsychological experiment suggests that the hippocampus does store an allocentric representation of spatial locations.

Quantum systems can realize content-addressable associative memory

It is presented how associative information processing can be implemented in quantum fields. Similarly to networks of coupled oscillators, quantum associative networks exploit correlations and phase-differences among “interfering eigen-wave-functions” for memorization and recognition of patterns.

An implementation efficient learning algorithm for adaptive control using associative content addressable memory

Three modifications to the Boxes-ASE/ACE reinforcement learning improves implementation efficiency and performance. A state history queue (SHQ) eliminates computations for temporally insignificant states. A dynamic link table only allocates control memory to states the system traverses. CMAC state association uses previous learning to decrease training time. Simulations show a 4-fold improvement in learning. The SHQ in a hardware implementation of the pole-cart balancer reduces computation time 11-fold.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Projection method formulations of Hopfield-type associative memory neural networks

We study certain neural network architectures, which admit to optical implementations, from the point of view of projection methods. This approach enables us to uncover some interesting facts about the stable states of such networks. For example, a Hopfield-type associative content-addressable memory (ACAM) using a hard-limiter nonlinearity has three kinds of possible stable states. If the hard-limiter is replaced by a unity-slope saturation nonlinearity, there are only two kinds of stable states. These and other results demonstrate the utility of using projection methods in analyzing the dynamic behavior of neural networks.

Learning algorithms for perceptions using back-propagation with selective updates

The error back-propagation algorithm for perceptrons is studied, and an extension of this algorithm that features selective learning is introduced. In selective learning, one of two selection criteria is used to screen the input data to improve the convergence property of the back-propagation algorithm. An associative content addressable memory using multilayer perceptrons is devised to demonstrate the improver convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Associative polarization-encoded optical shadow casting: gray-level image encoding for serial and parallel operations

An efficient algorithm for designing a serial and parallel multioutput logic unit using an associative polarization-encoded optical shadow-casting technique is presented. Devices designed as such have a unique contentaddressable associative memory where each of the memory locations has dual addresses. For illustration, the proposed algorithm has been used to design a serial-output and a parallel-output 2-bit gray adder.

Internal representations for associative memory

We describe a class of feed forward neural network models for associative content addressable memory (ACAM) which utilize sparse internal representations for stored data. In addition to the input and output layers, our networks incorporate an intermediate processing layer which serves to label each stored memory and to perform error correction and association. We study two classes of internal label representations: the unary representation and various sparse, distributed representations. Finally, we consider storage of sparse data and sparsification of data. These models are found to have advantages in terms of storage capacity, hardware efficiency, and recall reliability when compared to the Hopfield model, and to possess analogies to both biological neural networks and standard digital computer memories.

Optical implementation of the Hopfield model for two-dimensional associative memory

Optical implementation of Hopfield's neural network model [Proc. Natl. Acad. Sci. USA 79, 2554 (1982)] for two-dimensional associative memory is discussed. Two-state neuron elements are represented by a twisted nematic liquid-crystal optical switch array, and three-dimensional holographic interconnections are realized with these elements. Unipolar connections, created by adding a constant to bipolar interconnections and compensating them with an input-dependent thresholding operation, are realized. The 16- (4 x 4) neuron system model acts as a content-addressable associative memory with error-correction capability.

Handprinted Chinese character recognition via neural networks

A new recognition scheme for handprinted Chinese Characters is presented, which is composed of two phases: a stroke recognizing phase in which a handprinted character is matched with a standard character whose code is the closest to that of the handprinted character. The main idea is to use neural networks instead of a digital computer to perform the task of recognition. The character recognizing neural network is based on the neural network model of a content addressable associative memory (CAAM) developed in this paper. In the CAAM model, one thing will remind us of another, and partial information can retrieve the whole. Simulation results demonstrating of the neural network model are presented. The model may be extended so that it will be also useful in understanding some other pattern analysis in biological systems.

Hopfield model associative memory with nonzero-diagonal terms in memory matrix

The discrete-valued neural network proposed by Hopfield requires zero-diagonal terms in the memory matrix so that the net evolves toward a local minimum of an energy function. For a version of this model with bipolar nodes and positive terms along the diagonal, the net evolves so that only updates that lower the energy by a sufficient amount are accepted. For a net programmed as an outer-product associative content-addressable memory, the version with nonzero-diagonal elements performs nearly identically to one with zero-diagonal terms, and the dropping of the zero-diagonal requirement is advantageous for optical implementation.

Optical implementation of the Hopfield model

Optical implementation of content addressable associative memory based on the Hopfield model for neural networks and on the addition of nonlinear iterative feedback to a vector–matrix multiplier is described. Numerical and experimental results presented show that the approach is capable of introducing accuracy and robustness to optical processing while maintaining the traditional advantages of optics, namely, parallelism and massive interconnection capability. Moreover a potentially useful link between neural processing and optics that can be of interest in pattern recognition and machine vision is established.