A new Algorithm for Implementing BSB-based Associative Memories

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model formulated as a set of independent classification tasks, which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like a surprising generalized Hebb's law. The performance of the SVM approach is compared to existing methods with non-symmetric connections, by some design examples