This paper provides new approach for nonvolatile Memristor-based Content Addressable Memory MCAM cell using memristor with CMOS processing technology in order to get high speed read/write operations within high packing density and low power dissipation. The proposed cell uses only two memristors as a memory cell and CMOS controlling circuitry which uses latching to decrease time required for wr...

This paper presents an application of an associative memory neural network to the complex task of labelling the parts of an image. The network identiies features in the image, and recalls associations between features and the objects which they comprise. The identiication of features is tolerant of noise and clutter in the image. Features are associated with parameterised descriptions of object...

In this paper, we propose a Kohonen feature map associative memory with area representation for sequential patterns. This model is based on the Kohonen feature map associative memory with area representation and the Kohonen feature map associative memory for temporal sequences. The proposed model can learn sequential patterns successively, and has robustness for damaged neurons. We carried out ...

A modular connectionist network is described that learns the German verb paradigm. The architecture of the network is in accordance with the rule{associative memory hypothesis proposed by Pinker (1991): it is composed of a connectionist short-term memory enabling it to process symbolic rules and an associative memory acting as a lexicon. The network successfully learns the German verb paradigm ...

We examine the functional hypothesis of bidirectional associative memory in a pair of reciprocally projecting cortical cell groups. Our simulation model features two-compartment neurons and synaptic weights formed by Hebbian learning of pattern pairs. After stimulation of a learned memory in one group we recorded the network activation. At high synaptic memory load (0.14 bit/synapse) we varied ...

The information capacity of Kanerva's Sparse, Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that tire total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or...

In this paper, an entropy based associative memory model will be proposed and applied to memory retrievals with an orthogonal learning model so as to compare with the conventional model based on the quadratic Lyapunov functional to be minimized during the retrieval process. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which...

An essential part of image analysis is the location and identiication of objects within the image. Noise and clutter make this identiication problematic, and the size of the image may present a computational problem. To overcome these problems, we use a window onto the image to focus onto small areas. Conventionally we still need to know the size of the object we are searching for in order to s...

Heuristics and biases program described some behaviors of human decision making as adaptive prerational tools that lead to different kinds of structural cognitive errors. Some criticism on this program relay over the absence of theoretical and mechanistic models of heuristics. Cognitive errors in medical diagnosis are widely described as biases provoked by the use of heuristics in realistic cli...

In this paper, we describe a resistive configurable associative memory (ReCAM) that enables selective approximation and asymmetric voltage overscaling to manage delivered efficiency. The ReCAM structure matches an input pattern with pre-stored ones by applying an approximate search on selected bit indices (bitline-configurable). To further reduce energy, we explore proper ReCAM sizing, various ...