Contextual Navigation Framework

A biologically-inspired computational framework for autonomous agents that integrates internal need management with spatial navigation.

This framework integrates two key biological principles...

• Allostatic Control: Managing competing internal needs (energy, hydration, security, mating) through neural competition
• Hippocampal Sequences: Generating predictive spatial trajectories toward learned goal locations

... as spiking neural networks (SNNs).

Architecture

The framework operates through a hierarchical architecture with the following main processing stages:

Internal Needs > Drive Competition > Context Selection > Spatial Planning > Motor Execution
       |                 |                   |                  |                 |
 [Homeostatic]    [Allostatic SNN]     [Context Layer]    [Hippocampus]     [Robot Action]

Publications

This work is part of the CAVAA (Counterfactual Assessment and Valuation for Awareness Architecture) project. Key references:

• Gönner, L., Vitay, J., & Hamker, F. H. (2017). Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model. Frontiers in Computational Neuroscience, 11. https://doi.org/10.3389/fncom.2017.00084
• Guerrero-Rosado, O., Amil, A. F., Freire, I. T., & Verschure, P. F. M. J. (2022). Drive competition underlies effective allostatic orchestration. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.1052998
• Guerrero-Rosado, O., Amil, A. F., Freire, I. T., Vinck, M. & Verschure, P. F. M. J. (2025). Biomimetic self-regulation in intrinsically motivated robots.
• Eliasmith, C., & Anderson, C. H. (2003). Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press.