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arxiv: 2604.15076 · v1 · submitted 2026-04-16 · 💻 cs.RO · cs.AI· cs.NE

NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning

Hibatallah Meliani , Khadija Slimani , Samira Khoulji This is my paper

Pith reviewed 2026-05-10 10:57 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.NE
keywords NEATnavigation cellspath planningroboticsdynamic environmentsneuroevolutionrecurrent neural networkshippocampus
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The pith

Navigation cells inspired by brain spatial cells serve as inputs to evolve recurrent networks for robot path planning in dynamic settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces NEAT-NC to enhance the NEAT algorithm by feeding it navigation cells modeled on biological place cells, grid cells, and related spatial representations from the hippocampus. These cells, combined with sensory data, allow the evolution of recurrent neural networks that generate paths in both unchanging and changing environments. The authors test the method across multiple static and dynamic scenarios and conclude it adapts effectively to complexity. A reader would care if this bio-inspired input structure reduces the need for hand-crafted rules in real-time robot navigation and game AI.

Core claim

NEAT-NC uses navigation cells as inputs and evolves recurrent neural networks to represent hippocampal spatial processing, delivering path planning that performs across static and dynamic test cases and supports real-time use in robotics and games.

What carries the argument

NEAT guided Navigation Cells (NEAT-NC), which supplies abstract biological spatial navigation cells as inputs to NEAT for evolving recurrent neural networks that output robot paths.

If this is right

  • The method handles both fixed and moving obstacles without requiring separate replanning logic.
  • Recurrent network topologies evolved this way maintain performance when the environment changes during execution.
  • The approach extends naturally to game characters that must navigate while other agents move.
  • Biological spatial cell models can be reused as modular inputs across different evolutionary robotics tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real hardware robots could compute navigation cell activations directly from onboard sensors such as LIDAR or cameras to close the loop from biology to control.
  • The same cell inputs might improve other neuroevolution methods beyond NEAT when path planning occurs in partially observable spaces.
  • If the cells prove robust, they offer a route to reduce reliance on explicit maps in favor of distributed spatial representations.

Load-bearing premise

Abstract navigation cells drawn from biology can function as stable, effective inputs to the NEAT process even when their exact sensor mapping and dynamic interactions remain unspecified.

What would settle it

A dynamic test scenario with moving obstacles in which the evolved network using navigation cells produces collisions or deadlocks at rates comparable to or worse than standard NEAT without those cells.

Figures

Figures reproduced from arXiv: 2604.15076 by Hibatallah Meliani, Khadija Slimani, Samira Khoulji.

Figure 3
Figure 3. Figure 3: Place and border cells reaction in different Scenar [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Elements recognized by the navigation cells of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Border and Place cells grid placement [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Environment 3 contains two dynamic obstacles [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 5
Figure 5. Figure 5: Environment 1 is a S maze with no dynamic obsta [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Environment 2 contains five dynamic obstacles [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Dunn test’s Critical Difference (CD) diagrams on [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Dunn test’s Critical Difference (CD) diagrams on [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
read the original abstract

To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction cells, border cells, and speed cells. All these cells, along with sensory inputs, enable an organism to explore the space around it. Inspired by these biological principles, we developed NEATNC, a Neuro-Evolution of Augmenting Topology guided Navigation Cells. The goal of the paper is to improve NEAT algorithm performance in path planning in dynamic environments using spatial cognitive cells. This approach uses navigation cells as inputs and evolves recurrent neural networks, representing the hippocampus part of the brain. The performance of the proposed algorithm is evaluated in different static and dynamic scenarios. This study highlights NEAT's adaptability to complex and different environments, showcasing the utility of biological theories. This suggests that our approach is well-suited for real-time dynamic path planning for robotics and games.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper introduces NEAT-NC, a neuroevolution approach that augments the NEAT algorithm with abstract navigation cells (place, grid, head-direction, border, and speed cells) modeled after hippocampal spatial representations. These cells, combined with sensory inputs, serve as inputs to evolve recurrent neural networks for robot path planning. The central claim is that this biologically inspired method improves performance over standard NEAT in both static and dynamic environments and is suitable for real-time applications in robotics and games, with evaluations across multiple scenarios.

Significance. If the unspecified implementation details were provided and the performance gains were demonstrated with quantitative metrics and baselines, the work could offer a concrete bridge between biological spatial cognition models and neuroevolutionary control, potentially improving robustness in dynamic path planning. No machine-checked proofs, reproducible code, or parameter-free derivations are described, so these strengths are absent from the current manuscript.

major comments (3)
  1. [Abstract] Abstract and methods description: The computation of navigation cells (e.g., place-cell activation functions, grid-cell coordinate transformations, sensor-to-cell mapping, and stability under robot motion or obstacle changes) is not specified with any equations, pseudocode, or algorithmic steps. This is load-bearing for the central claim because without these details the inputs to the NEAT-evolved RNNs cannot be reproduced or verified as the source of any reported improvements.
  2. [Abstract] Abstract: No quantitative results, baselines (e.g., standard NEAT, A*, D*), metrics (success rate, path length, computation time), error bars, or statistical tests are provided for the static and dynamic scenarios. This prevents assessment of whether the claimed performance gains actually hold.
  3. [Abstract] Abstract: The integration of the navigation-cell inputs into the NEAT evolutionary process (network topology augmentation, fitness function, recurrent connections representing hippocampus) is described only at a conceptual level with no activation functions, coordinate transformations, or pseudocode, rendering the “hippocampus representation” an untested label rather than a verifiable mechanism.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We agree that the current manuscript lacks sufficient implementation details and quantitative evaluations, which limits the ability to fully assess and reproduce the claims. We will revise the manuscript to address all points raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract and methods description: The computation of navigation cells (e.g., place-cell activation functions, grid-cell coordinate transformations, sensor-to-cell mapping, and stability under robot motion or obstacle changes) is not specified with any equations, pseudocode, or algorithmic steps. This is load-bearing for the central claim because without these details the inputs to the NEAT-evolved RNNs cannot be reproduced or verified as the source of any reported improvements.

    Authors: We acknowledge that the navigation cell computations were presented at a high level in the original manuscript. The implementations draw from established models in the literature (Gaussian place-cell activations, hexagonal grid-cell patterns, etc.), but specific equations, mappings, and stability analysis were not included. In the revised version, we will add a dedicated Methods subsection with the precise activation functions, coordinate transformations, sensor-to-cell mappings, and considerations for stability under robot motion and dynamic obstacles, accompanied by equations and pseudocode for full reproducibility. revision: yes

  2. Referee: [Abstract] Abstract: No quantitative results, baselines (e.g., standard NEAT, A*, D*), metrics (success rate, path length, computation time), error bars, or statistical tests are provided for the static and dynamic scenarios. This prevents assessment of whether the claimed performance gains actually hold.

    Authors: The original manuscript relied on qualitative scenario demonstrations rather than formal quantitative reporting. We agree this is a significant gap for evaluating the performance claims. The revised manuscript will include a new Results subsection with quantitative metrics (success rate, path length, computation time) across static and dynamic environments, direct comparisons against standard NEAT, A*, and D* baselines, error bars from repeated trials, and statistical significance tests. revision: yes

  3. Referee: [Abstract] Abstract: The integration of the navigation-cell inputs into the NEAT evolutionary process (network topology augmentation, fitness function, recurrent connections representing hippocampus) is described only at a conceptual level with no activation functions, coordinate transformations, or pseudocode, rendering the “hippocampus representation” an untested label rather than a verifiable mechanism.

    Authors: We will expand the description of the integration mechanism in the revised Methods and Algorithm sections. This will include explicit details on how navigation-cell outputs augment the NEAT input layer and topology evolution, the definition of the fitness function (incorporating path efficiency, collision avoidance, and goal reaching), the role of recurrent connections in modeling hippocampal-like dynamics, and the relevant activation functions and transformations, all supported by equations and pseudocode. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual description with no equations or derived predictions

full rationale

The manuscript presents a high-level conceptual proposal for integrating abstract navigation cells (place/grid/etc.) as inputs to NEAT-evolved RNNs, without any equations, parameter fittings, uniqueness theorems, or self-cited derivations. No load-bearing step reduces a claimed result to its own inputs by construction; performance claims rest on unreported empirical evaluations rather than a closed mathematical chain. The absence of a derivation to inspect means no circularity patterns apply.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven assumption that biological navigation mechanisms can be abstracted into usable inputs for evolutionary neural controllers without loss of utility or introduction of instability.

axioms (1)
  • domain assumption Navigation cells modeled after place, grid, and related brain cells can be effectively simulated and used as stable inputs to NEAT
    Invoked in the description of the approach and its evaluation in dynamic environments.

pith-pipeline@v0.9.0 · 5461 in / 1219 out tokens · 42158 ms · 2026-05-10T10:57:04.286351+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [1]

    Place cells: The brain cells that help us navigate the world.The Nobel collection, Volume 2, page 93, 2023

    John O’Keefe. Place cells: The brain cells that help us navigate the world.The Nobel collection, Volume 2, page 93, 2023

    work page 2023

  2. [2]

    Dendritic mechanisms of hippocampal place field formation.Current opinion in neurobiology, 54:1–11, 2019

    Mark EJ Sheffield and Daniel A Dombeck. Dendritic mechanisms of hippocampal place field formation.Current opinion in neurobiology, 54:1–11, 2019

    work page 2019

  3. [3]

    The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat.Brain research, 1971

    John O’Keefe and Jonathan Dostrovsky. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat.Brain research, 1971

    work page 1971

  4. [4]

    Space in the brain: how the hippocampal formation supports spatial cognition.Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1635):20120510, 2014

    Tom Hartley, Colin Lever, Neil Burgess, and John O’Keefe. Space in the brain: how the hippocampal formation supports spatial cognition.Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1635):20120510, 2014

    work page 2014

  5. [5]

    Hippocampus-independent phase precession in entorhinal grid cells.Na- ture, 453(7199):1248–1252, 2008

    Torkel Hafting, Marianne Fyhn, Tora Bonnevie, May-Britt Moser, and Edvard I Moser. Hippocampus-independent phase precession in entorhinal grid cells.Na- ture, 453(7199):1248–1252, 2008

    work page 2008

  6. [6]

    Direct recordings of grid-like neuronal activity in human spatial navigation.Nature neuroscience, 16(9):1188–1190, 2013

    Joshua Jacobs, Christoph T Weidemann, Jonathan F Miller, Alec Solway, John F Burke, Xue-Xin Wei, Nanthia Suthana, Michael R Sperling, Ashwini D Sharan, Itzhak Fried, et al. Direct recordings of grid-like neuronal activity in human spatial navigation.Nature neuroscience, 16(9):1188–1190, 2013

    work page 2013

  7. [7]

    The head direction signal: origins and sensory-motor integration

    Jeffrey S Taube. The head direction signal: origins and sensory-motor integration. Annu. Rev. Neurosci., 30(1):181–207, 2007

    work page 2007

  8. [8]

    Grid cells in pre-and parasubiculum.Nature neuroscience, 13(8):987–994, 2010

    Charlotte N Boccara, Francesca Sargolini, Veslemøy Hult Thoresen, Trygve Sol- stad, Menno P Witter, Edvard I Moser, and May-Britt Moser. Grid cells in pre-and parasubiculum.Nature neuroscience, 13(8):987–994, 2010

    work page 2010

  9. [9]

    Bound- ary vector cells in the subiculum of the hippocampal formation.Journal of Neu- roscience, 29(31):9771–9777, 2009

    Colin Lever, Stephen Burton, Ali Jeewajee, John O’Keefe, and Neil Burgess. Bound- ary vector cells in the subiculum of the hippocampal formation.Journal of Neu- roscience, 29(31):9771–9777, 2009

    work page 2009

  10. [10]

    Representation of geometric borders in the entorhinal cortex.Science, 322(5909):1865–1868, 2008

    Trygve Solstad, Charlotte N Boccara, Emilio Kropff, May-Britt Moser, and Edvard I Moser. Representation of geometric borders in the entorhinal cortex.Science, 322(5909):1865–1868, 2008

    work page 2008

  11. [11]

    Speed cells in the medial entorhinal cortex.Nature, 523(7561):419–424, 2015

    Emilio Kropff, James E Carmichael, May-Britt Moser, and Edvard I Moser. Speed cells in the medial entorhinal cortex.Nature, 523(7561):419–424, 2015

    work page 2015

  12. [12]

    Hippocampal place cells have goal-oriented vector fields during navigation.Nature, 607(7920):741–746, 2022

    Jake Ormond and John O’Keefe. Hippocampal place cells have goal-oriented vector fields during navigation.Nature, 607(7920):741–746, 2022

    work page 2022

  13. [13]

    Tempga: A temperature- inspired adaptive genetic algorithm for solving 7dof inverse kinematics problems

    Hibatallah Meliani, Khadija Slimani, and Samira Khoulji. Tempga: A temperature- inspired adaptive genetic algorithm for solving 7dof inverse kinematics problems. IEEE Access, 13:179185–179199, 2025

    work page 2025

  14. [14]

    A path planning framework for robots based on improved parallel sampling rrt and offset guidance dwa.IEEE Sensors Journal, 2025

    Yaowei Hu, Xufei Chen, Pingping Tang, Hui Zhang, Jiong Jin, and Shiwen Mao. A path planning framework for robots based on improved parallel sampling rrt and offset guidance dwa.IEEE Sensors Journal, 2025

    work page 2025

  15. [15]

    Path planning of mobile robot based on dual-layer fuzzy control and improved genetic algorithm.Symmetry, 17(4):609, 2025

    Yangxin Teng, Tingping Feng, Changlin Song, Junmin Li, Simon X Yang, and Hongjun Zhu. Path planning of mobile robot based on dual-layer fuzzy control and improved genetic algorithm.Symmetry, 17(4):609, 2025

    work page 2025

  16. [16]

    Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method.Expert Systems with Applications, page 127808, 2025

    Yuchao Wang, Kelin Tong, Chunhai Fu, Yuhang Wang, Qiuhua Li, Xingni Wang, Yunzhe He, and Lijia Xu. Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method.Expert Systems with Applications, page 127808, 2025

    work page 2025

  17. [17]

    A fusion algorithm of robot path planning based on improved gray wolf algorithm and dynamic window approach

    Fei Liu, Xiankun Wu, Li Ma, and Dazhang You. A fusion algorithm of robot path planning based on improved gray wolf algorithm and dynamic window approach. Electronics, 14(2):302, 2025

    work page 2025

  18. [18]

    Robot path planning using fuzzy tsukamoto simulated annealing

    Hibatallah Meliani, Said Ohamouddou, Hanaa El Afia, Mohamed Hamza Boulaich, and Abdellatif El Afia. Robot path planning using fuzzy tsukamoto simulated annealing. In2024 Mediterranean Smart Cities Conference (MSCC), pages 1–4. IEEE, 2024

    work page 2024

  19. [19]

    Real-time and energy-aware uav routing: A scalable dar approach for future 6g systems.Computers, Materials and Continua, 85(3):4667–4686, 2025

    Khadija Slimani, Samira Khoulji, Hamed Taherdoost, and Mohamed Larbi Kerkeb. Real-time and energy-aware uav routing: A scalable dar approach for future 6g systems.Computers, Materials and Continua, 85(3):4667–4686, 2025

    work page 2025

  20. [20]

    Optimal path planning approach based on q-learning algorithm for mobile robots.Applied Soft Computing, 97:106796, 2020

    Abderraouf Maoudj and Abdelfetah Hentout. Optimal path planning approach based on q-learning algorithm for mobile robots.Applied Soft Computing, 97:106796, 2020

    work page 2020

  21. [21]

    Cross-regional path planning based on improved q-learning with dynamic exploration factor and heuristic reward value.Expert Systems with Applications, 260:125388, 2025

    Ying Zhong and Yanhong Wang. Cross-regional path planning based on improved q-learning with dynamic exploration factor and heuristic reward value.Expert Systems with Applications, 260:125388, 2025

    work page 2025

  22. [22]

    Mobile robot path planning using deep deterministic policy gradient with differential gaming (ddpg-dg) exploration.Cognitive Robotics, 4:156–173, 2024

    Shripad V Deshpande, R Harikrishnan, Babul Salam KSM Kader Ibrahim, and Mahesh Datta Sai Ponnuru. Mobile robot path planning using deep deterministic policy gradient with differential gaming (ddpg-dg) exploration.Cognitive Robotics, 4:156–173, 2024

    work page 2024

  23. [23]

    Neuroevo- lutionary multi-objective approaches to trajectory prediction in autonomous vehicles

    Fergal Stapleton, Edgar Galván, Ganesh Sistu, and Senthil Yogamani. Neuroevo- lutionary multi-objective approaches to trajectory prediction in autonomous vehicles. InProceedings of the Genetic and Evolutionary Computation Conference Companion, pages 675–678, 2022

    work page 2022

  24. [24]

    Near-driven autonomous rover nav- igation in complex environments: Extensions to urban search-and-rescue and industrial inspection.arXiv preprint arXiv:2504.17794, 2025

    Dhadkan Shrestha and Lincoln Bhattarai. Near-driven autonomous rover nav- igation in complex environments: Extensions to urban search-and-rescue and industrial inspection.arXiv preprint arXiv:2504.17794, 2025

    work page arXiv 2025

  25. [25]

    Neat-based 3d path planning for mobile robotic arms in ndt with offline inverse kinematics validation.MATEC Web of Conferences, 2025

    Mengyuan Zhang, Qingping Yang, Mark Sutcliffe, and Ian Nicholson. Neat-based 3d path planning for mobile robotic arms in ndt with offline inverse kinematics validation.MATEC Web of Conferences, 2025

    work page 2025

  26. [26]

    Towards obstacle- avoiding control of planar snake robots exploring neuro-evolution of augmenting topologies.arXiv preprint arXiv:2511.12148, 2025

    Advik Sinha, Akshay Arjun, Abhijit Das, and Joyjit Mukherjee. Towards obstacle- avoiding control of planar snake robots exploring neuro-evolution of augmenting topologies.arXiv preprint arXiv:2511.12148, 2025

    work page arXiv 2025

  27. [27]

    Reinforced neat algorithms for au- tonomous rover navigation in multi-room dynamic scenario.Fire, 8(2):41, 2025

    Dhadkan Shrestha and Damian Valles. Reinforced neat algorithms for au- tonomous rover navigation in multi-room dynamic scenario.Fire, 8(2):41, 2025. Meliani et al

    work page 2025

  28. [28]

    Towards a predictive bio-inspired navigation model.Information, 12(3):100, 2021

    Simon Gay, Kévin Le Run, Edwige Pissaloux, Katerine Romeo, and Christèle Lecomte. Towards a predictive bio-inspired navigation model.Information, 12(3):100, 2021

    work page 2021

  29. [29]

    A spatial cognitive cells inspired goal-directed navigation model

    Lingfang Hu, Kuangrong Hao, Xin Cai, and Lei Chen. A spatial cognitive cells inspired goal-directed navigation model. In2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pages 211–215. IEEE, 2019

    work page 2019

  30. [30]

    A bio-inspired goal-directed cognitive map model for robot navigation and exploration.IEEE Transactions on Cognitive and Developmental Systems, 2025

    Matthew Hicks, Tingjun Lei, Chaomin Luo, Daniel W Carruth, and Zhuming Bi. A bio-inspired goal-directed cognitive map model for robot navigation and exploration.IEEE Transactions on Cognitive and Developmental Systems, 2025

    work page 2025

  31. [31]

    Brain-like path planning algorithm based on spik- ing neural network

    Jilun Zhang and Ying Liu. Brain-like path planning algorithm based on spik- ing neural network. InProceedings of the 2025 6th International Conference on Computer Information and Big Data Applications, pages 1379–1386, 2025

    work page 2025

  32. [32]

    Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling.Elife, 12:RP84141, 2024

    Tony Zhang, Matthew Rosenberg, Zeyu Jing, Pietro Perona, and Markus Meister. Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling.Elife, 12:RP84141, 2024

    work page 2024

  33. [33]

    Neurobiologically in- spired mobile robot navigation and planning.Frontiers in neurorobotics, 1:104, 2007

    Nicolas Cuperlier, Mathias Quoy, and Philippe Gaussier. Neurobiologically in- spired mobile robot navigation and planning.Frontiers in neurorobotics, 1:104, 2007

    work page 2007