Executing Declarative Parallel Representations of Sequences with Temporal Pooling

A Self-Organising Temporal Pooling (SOTP) network has been shown to be capable of forming declarative parallel representations of sequential events and chunking these events without supervision. However, such a network currently cannot take these declarative representations and execute the associated sequence; it is strictly a one-way sequence chunker and encoder. We present a modified model that can convert the lateral weights of the trained network into a primacy gradient usable by methods such as Competitive Queueing (CQ), thus granting the network a method for executing learned sequences. The resulting model has several benefits over traditional CQ. We further present an advanced method of executing sequences via SOTP itself, resulting in less error than CQ whilst being more flexible in replaying sequences from datasets with variable sequence lengths.