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Cognitive Domain Ontologies Based on Loihi Spiking Neurons Implemented Using a Confabulation Inspired Network

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). Given the number of possible solutions in the problems tasked to CDOs, determining the optimal solutions can be very time consuming. In this work we show how problems that are often solved using CDOs can be carried out using spiking neurons. Furthermore, this work discusses using the Intel Loihi manycore spiking neural network processor to solve CDOs using a technique inspired by a confabulation network. This work demonstrates the feasibility of implementing CDOs on embedded, low power, neuromorphic spiking hardware.