Bidirectional Associative Memory for Short-term Memory Learning

Bidirectional Associative Memory for Short-term Memory Learning Christophe Tremblay (ctrem040@uottawa.ca) School of Psychology, 136 Jean-Jacques Lussier University of Ottawa, ON K1N 6N5 Canada Nareg Berberian (nberb062@uottawa.ca) School of Psychology, 136 Jean-Jacques Lussier University of Ottawa, ON K1N 6N5 Canada Sylvain Chartier (sylvain.chartier@uottawa.ca) School of Psychology, 136 Jean-Jacques Lussier University of Ottawa, ON K1N 6N5 Canada Abstract Previous research has shown that Bidirectional Associative Memories (BAM), a special type of artificial neural network, can perform various types of associations that human beings are able to perform with little effort. However, considering a simple association problem, such as associating faces with names, iterative type BAM networks usually take hundreds and sometimes thousands of learning trials to encode such associations correctly, whereas humans in some conditions learn much faster. The present study therefore proposes an adjustment to a particular type of BAM network that increases its performance in a rapid learning condition while processing memory capacity is limited. Results show that the modification to the original learning rule of the BHM leads to improved performance when rapid learning is required. Moreover, the model preserves its high memory load capacity in standard learning. This study could lead to improved cognitive models that can adapt their behavior in function of the contextual conditions. Keywords: Artificial intelligence; Connectionist models; Bidirectional associative memory; Short-term memory learning. Introduction Like human beings, artificial neural networks can discriminate, identify, and categorize perceptual patterns (Faussett, 1994; Haykin, 2009). Bidirectional Associative Memories (BAMs) have been proposed as models of neurodynamics. As such, they are able to develop attractors allowing the network to perform various types of recall under noisy conditions and successfully carry out pattern completion. Many advances were made since Kosko introduced the BAM in 1988; see Acevedo-Mosqueda, Yanez-Marquez & Acevedo-Mosqueda (2013) for a review. Although the majority of improvements made BAM models better suited for learning a wider set of associations and brought greater performance, such improvements have been at the expense of learning times. Nowadays, BAM models often take hundreds and sometimes thousands of learning cycles to correctly associate patterns. Consequently, researchers have attempted to enhance convergence times and performance (Nong & Bui, 2012). Several one-shot learning rules were developed, showing almost perfect performance in some cases (Ritter, Diaz-Deleon, & Sussner, 1999; Wu & Pados, 2000, Acedo, Yanez, & Lopez, 2006a; 2006b). Although these improvements have successfully overcome the limitations of the model presented previously, these models were most often applied to engineering problems rather than providing legitimate models of cognition. For example, good connectionist models of cognition should adapt their connection weights locally, without homuncular knowledge, backpropagation of the error or complex learning procedure (O'Reilly, 1998). Therefore, models should show the ability to learn quickly with excellent performance, while possessing limited memory capacity when encoding time is short (one shot or few shot learning). On the other hand, they should present good performance and good memory load capacity when the encoding time allowed is longer. The present study proposes the introduction of a recency parameter in a modified BAM network (Chartier & Boukadoum, 2006b; 2011), which leads to improved performance in rapid learning situations, but with limited storing capacity. The proposed process therefore allows for learning in conditions where a limited number of iterations is required. The remainder of the paper is divided as follows: The background section describes the architecture of the model and the internal properties of the network, while the method section presents the learning and recall procedures of the model. The results section describes the recall performance tasks of the model under various level of noise. The final section discusses the results and provides a conclusion to the study. Background The model proposed by Chartier & Boukadoum (2006b; 2011) uses an unique matrix for each layer. This Bidirectional Heteroassociative Memory (BHM) is able to learn correlated patterns for bipolar patterns as well as for real-valued patterns. Architecture The network is made of two Hopfield-like neural networks interconnected in a head-to-tail fashion, providing a