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Updated: Jun 6, 2026
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Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
A neuromorphic model of spatial lookahead planning
Richard Ivey1, Daniel Bullock, Stephen Grossberg
1Department of Cognitive and Neural Systems, Center for Adaptive Systems and Center of Excellence for Learning in Education, Science, and Technology, Boston University, 677 Beacon Street, Boston, MA 02215, United States.
Summary
Organisms create complex spatial plans by rapidly adapting to novel obstacles. A new neuromorphic system prepares multistep lookahead plans before movement, outperforming other models in varied environments.
Area of Science:
- Neuroscience
- Cognitive Science
- Artificial Intelligence
Background:
- Organisms require adaptive spatial planning to navigate complex, changing environments with obstacles.
- Animals can form multistep spatial plans before initiating movement, even in novel layouts.
- Understanding the neural computations underlying such lookahead planning is crucial.
Purpose of the Study:
- To introduce a novel neuromorphic system for spatial lookahead planning.
- To investigate the neural mechanisms enabling preparatory multistep plans in obstructed 2D scenes.
- To compare the proposed system with existing spatial planning models.
Main Methods:
- Developed a neuromorphic system combining recurrent shunting competitive networks, visuo-spatial diffusion, and inhibition-of-return.
- The system iteratively prepares multistep trajectories to a goal state, considering obstacles.
- Planned trajectories are stored in sequential working memory and enacted via competitive queuing.
Main Results:
- The proposed system demonstrates robustness to environmental variations and obstacle configurations.
- It successfully plans feasible routes where other models fail.
- Simulation results align with neurophysiological and behavioral data from primate spatial lookahead studies.
Conclusions:
- The novel neuromorphic system effectively models preparatory multistep lookahead planning.
- It offers a biologically plausible explanation for spatial cognition in complex environments.
- The model provides insights into the neural basis of spatial attention and planning.
