Abstract: A technical solution is described for implementing a computer-executed system of association memory matrices to replace the proximal layers of a convolutional neural network (CNN). An example method includes configuring one Associative Memory Matrix (AMM) for each configured layer in the CNN. This one-to-one conversion method motivates the name to the product: the Joint Proximity Association Template (JPAT) for Neural Networks. The invention is a numerically stable soft-ware based implementation that (1) reduces the long training times, (2) reduces the execution time, and (3) produces bidirectional intra-layer connections and potentially, inter-layer connections as well. The method further includes, potentially, forming a single AMM, from the multiple AMMs corresponding to the multiple and proximal layers of the CNN, in anticipation of the well-known Universal Approximation Theorem.

Abstract: The invention is an add-on implementation of a stabilized association memory matrix system to an existing convolutional neural network framework. This invention emulates the intra-action and the inter-action of the cognitive processes of the (logical) left-brain and (intuitive) right-brain. The invention is a numerically stable soft-ware based implementation that (1) reduces the long training times, (2) reduces the execution time, and (3) produces intralayer and interlayer connections. The implementation of this joint processing architecture is designed to take an existing hierarchy of stepped based processes, add next to it a parallel hierarchy of associative memory processes, and then connect the two processes by another set of associative memory processes. Or, the stepped-based process may be replaced with additional associative memory processes to enhance the emulation of several bidirectional intralayer and interlayer cognitive process communication.