Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks

concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure.

Circuit Implementation, Operation, and Simulation of Multivalued Nonvolatile Static Random Access Memory Using a Resistivity Change Device

We proposed and computationally analyzed a multivalued, nonvolatile SRAM using a ReRAM. Two reference resistors and a programmable resistor are connected to the storage nodes of a standard SRAM cell. The proposed 9T3R MNV-SRAM cell can store 2 bits of memory. In the storing operation, the recall operation and the successive decision operation of whether or not write pulse is required can be performed simultaneously. Therefore, the duration of the decision operation and the circuit are not required when using the proposed scheme. In order to realize a stable recall operation, a certain current (or voltage) is applied to the cell before the power supply is turned on. To investigate the process variation tolerance and the accuracy of programmed resistance, we simulated the effect of variations in the width of the transistor of the proposed MNV-SRAM cell, the resistance of the programmable resistor, and the power supply voltage with 180 nm 3.3 V CMOS HSPICE device models.