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Updated: Apr 23, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Multistate network model for the pathfinding problem with a self-recovery property.

Kei-Ichi Ueda¹, Masaaki Yadome², Yasumasa Nishiura²

¹Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan.

Neural Networks : the Official Journal of the International Neural Network Society

|September 22, 2014

Summary

This summary is machine-generated.

This study introduces a continuous pathfinding model for acyclic graphs. The system autonomously finds paths and self-recovers from connection failures, with inhibitory interactions improving search efficiency.

Keywords:

Attractors Dynamical systems Pathfinding

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Area of Science:

Computer Science
Artificial Intelligence
Graph Theory

Background:

Pathfinding algorithms are crucial for navigation and network routing.
Existing models often struggle with dynamic environments and real-time adaptation.
Continuous models offer potential for more robust and efficient pathfinding solutions.

Purpose of the Study:

To propose a novel continuous model for autonomous pathfinding in acyclic graphs.
To demonstrate the model's ability to find stable paths between specified vertices.
To investigate the self-recovery capabilities and performance enhancement through inhibitory interactions.

Main Methods:

Development of a continuous mathematical model for pathfinding.
Analysis of acyclic graphs with unidirectional edges.
Simulation and evaluation of the model's performance, including self-recovery and search time.
Implementation of inhibitory interactions to optimize search processes.

Main Results:

The proposed continuous model successfully finds stable paths in acyclic graphs.
The system exhibits a self-recovery property, adapting to terminated connections.
Inhibitory interactions were shown to significantly improve pathfinding search time.

Conclusions:

The continuous pathfinding model offers a robust and adaptive solution for navigation problems.
The self-recovery feature enhances system reliability in dynamic environments.
Optimizing with inhibitory interactions presents a viable strategy for efficient pathfinding.