Cortical Learning Algorithms with Predictive Coding for a Systems-Level Cognitive Architecture

Human-level intelligent agents must autonomously navigate complex, dynamic, uncertain environments with bounded time and memory. This requires that they continually update a hierarchical, dynamic, probabilistic (uncertain) internal model of their current situation, via approximate Bayesian inference, incorporating both the sensory data and a generative model of its causes. Such modeling requires suitable representation at multiple levels of abstraction from the subsymbolic, sensory level to the most abstract conceptual representation. To guide our approach, we identify principles for perceptual representation, perceptual inference, and the associated learning processes. Based on these principles, a predictive coding extension to the HTM Cortical Learning Algorithms (CLA), termed PC-CLA, is proposed as a foundational building block for the systems-level LIDA cognitive architecture. PC-CLA fleshes out LIDA’s internal representations, memory, learning and attentional processes; and takes an initial step towards the comprehensive use of distributed and probabilistic (uncertain) representation throughout the architecture.