Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing

TL;DR

This paper introduces a modular approach combining cognitive map learners with hyperdimensional computing to enable independent training and flexible, parallel processing of graph-based representations in artificial neural networks.

Contribution

It presents a novel modular framework that segregates graph knowledge from target selection using hyperdimensional computing, allowing independent training and parallel experience modeling.

Findings

Hierarchical CMLs can be engineered with HDC for complex graph traversal.

Multiple experience models can be combined in superposition without retraining.

The modular CML-HDC system supports application-specific stimulus symbols without retraining.

Abstract

Biological organisms must learn how to control their own bodies to achieve deliberate locomotion, that is, predict their next body position based on their current position and selected action. Such learning is goal-agnostic with respect to maximizing (minimizing) an environmental reward (penalty) signal. A cognitive map learner (CML) is a collection of three separate yet collaboratively trained artificial neural networks which learn to construct representations for the node states and edge actions of an arbitrary bidirectional graph. In so doing, a CML learns how to traverse the graph nodes; however, the CML does not learn when and why to move from one node state to another. This work created CMLs with node states expressed as high dimensional vectors suitable for hyperdimensional computing (HDC), a form of symbolic machine learning (ML). In so doing, graph knowledge (CML) was segregated from target node selection (HDC), allowing each ML approach to be trained independently. The first approach used HDC to engineer an arbitrary number of hierarchical CMLs, where each graph node state specified target node states for the next lower level CMLs to traverse to. Second, an HDC-based stimulus-response experience model was demonstrated per CML. Because hypervectors may be in superposition with each other, multiple experience models were added together and run in parallel without any retraining. Lastly, a CML-HDC ML unit was modularized: trained with proxy symbols such that arbitrary, application-specific stimulus symbols could be operated upon without retraining either CML or HDC model. These methods provide a template for engineering heterogenous ML systems.