A multiple BAM for hetero-association and multisensory integration modelling

Abstract

We present in this article a dynamic neural network that works as a memory for multiple associations. Heterogeneous pairs of patterns can be tied together through learning within this memory, and recalled easily. Starting from Kosko's bidirectional associative memory, we modify some fundamental features of the network (topology and learning algorithm). We show empirically that this network has a high storage capacity and is only weakly dependent upon learning hyperparameters. We demonstrate its robustness to corrupted or missing data. We finally present results from experiments where this network is used as a multisensory associative memory.