Biomorphic navigation system version

Yu. A. Malichev ; V. G. Jahno

Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 32 (2024) no. 5, pp. 606-624.

Résumé

The purpose of this work is to create and study the dynamics of the functioning of a biorelevant visual navigation system. Methods. The work uses simultaneous navigation and mapping systems RatSLAM and Orb-SLAM. The RatSLAM system is a biorelevant model of visual navigation in the rodent hippocampus. The Orb-SLAM system is a simultaneous navigation and mapping system that works on the principle of searching and tracking changes in the position of key points in the image. Results. The article presents a version of a modified visual navigation system. The system consists of a visual odometry module based on the Orb-SLAM system, as well as a mapping and loop closure module based on the RatSLAM system. This allows you to combine the localization accuracy of systems operating on the principle of tracking key points in the image and neural filtering of biorelevant systems. Using the constructed system, location estimates were obtained on public and new data sets. Conclusion. The constructed visual navigation system determines the location of the subject (video camera) in space, which is in good agreement with the ground truth location data.

Détail

Keywords: simultaneous localization and mapping systems, path integration, landmarks, neural networks

Bibliographie

[1] Tolman E. C., “Cognitive maps in rats and men”, Psychological Review, 55:4 (1948), 189–208 | DOI

[2] McNaughton B. L., Battaglia F. P., Jensen O., Moser E. I., Moser M.-B., “Path integration and the neural basis of the “cognitive map””, Nature Reviews Neuroscience, 7 (2006), 663–678 | DOI

[3] Mittelstaedt H., Mittelstaedt M.-L., “Homing by path integration”, Avian Navigation, Proceedings in Life Sciences, eds. Papi F., Wallraff H. G., Springer, Berlin, Heidelberg, 1982, 290–297 | DOI

[4] Etienne A. S., Maurer R., Boulens V., Levy A., Rowe T., “Resetting the path integrator: a basic condition for route-based navigation”, Journal of Experimental Biology, 207 (2004), 1491–508 | DOI

[5] O’Keefe J., Dostrovsky J., “The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat”, Brain Research, 34 (1971), 171–175 | DOI

[6] Muller R. U, Ranck J. B. Jr, Taube J. S., “Head direction cells: properties and functional significance”, Current Opinion in Neurobiology, 6 (1996), 196–206 | DOI

[7] Taube J. S., “The head direction signal: Origins and sensory-motor integration”, Annual Review of Neuroscience, 30 (2007), 181–207 | DOI | MR

[8] Amit D. J., Modeling Brain Function: The World of Attractor Neural Networks, Cambridge University Press, New York, NY, 1989 | DOI | MR | Zbl

[9] Moser E. I., Roudi Y., Witter M. P., Kentros C., Bonhoeffer T., Moser M.-B., “Grid cells and cortical representation”, Nature Reviews Neuroscience, 15 (2014), 466–481 | DOI

[10] Hafting T., Fyhn M., Molden S., Moser M.-B., Moser E. I., “Microstructure of a spatial map in the entorhinal cortex”, Nature, 436 (2005), 801–806 | DOI

[11] Milford M. J., Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models, Springer, Vol 41 Springer Tracts in Advanced Robotics Berlin, Heidelberg, 2008, 196 pp. | DOI | MR | Zbl

[12] Eichenbaum H., “The role of the hippocampus in navigation is memory”, Journal of Neurophysiology, 117:4 (2017), 1785–1796 | DOI

[13] Gu Y., Lewallen S., Kinkhabwala A. A., Domnisoru C., Yoon K., Gauthier J. L., Fiete I. R., Tank D. W., “A map-like micro-organization of grid cells in the medial entorhinal cortex”, Cell, 175 (2018), 736–750 | DOI

[14] Poulter S., Hartley T., Lever C., “The neurobiology of mammalian navigation”, Current Biology, 28 (2018), R1023-R1042 | DOI

[15] Park E., Dvorak D., Fenton A. A., “Ensemble place codes in hippocampus: CA1, CA3, and dentate gyrus place cells have multiple place fields in large environments”, PLoS One, 6 (2011), e22349 | DOI

[16] Goode T. D., Tanaka K. Z., Sahay A., McHugh T. J., “An integrated index: Engrams, place cells, and hippocampal memory”, Neuron, 107 (2020), 805–820 | DOI

[17] Epstein R. A., Patai E. Z., Julian J. B., Spiers H. J., “The cognitive map in humans: spatial navigation and beyond”, Nature Neuroscience, 20 (2017), 1504–1513 | DOI

[18] Sarel A., Finkelstein A., Las L., Ulanovsky N., “Vectorial representation of spatial goals in the hippocampus of bats”, Science, 355 (2017), 176–180 | DOI

[19] Savelli F., Knierim J. J., “Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions”, Journal of Experimental Biology, 222 (2019), jeb188912 | DOI

[20] Rolls E. T., Stringer S. M., Elliot T., “Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning”, Network, 17 (2006), 447–65 | DOI | MR

[21] Si B., Treves A., “The role of competitive learning in the generation of DG fields from EC inputs”, Cognitive Neurodynamics, 3 (2009), 177–187 | DOI

[22] Savelli F., Knierim J. J., “Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields”, Journal of Neurophysiology, 103 (2010), 3167–83 | DOI

[23] Danjo T., Toyoizumi T., Fujisawa S., “Spatial representations of self and other in the hippocampus”, Science, 359 (2018), 213–218 | DOI

[24] Lian Y., Burkitt A. N., “Learning spatiotemporal properties of hippocampal place cells”, eNeuro, 9 (2022) | DOI

[25] Tsukerman V. D., Cheshkov G. N., “Osnovy nelineinoi dinamiki sensornogo vospriyatiya. I. Fazovoe kodirovanie v ostsillyatornykh setyakh”, Neirokompyutery: razrabotka, primenenie, 2002, no. 7–8, 65–72

[26] Tsukerman V. D., “Matematicheskaya model fazovogo kodirovaniya sobytii v mozge”, Matematicheskaya biologiya i bioinformatika, 1:1 (2006), 97–107 | DOI | MR

[27] Tsukerman V. D., Eremenko Z. S., Karimova O. V., Kulakov S. V., Sazykin A. A., “Kognitivnaya neirodinamika dvukh strategii navigatsionnogo povedeniya organizmov”, Izvestiya vuzov. PND, 19:6 (2011), 96–108 | DOI | Zbl

[28] Tsukerman V. D., Kharybina Z. S., Kulakov S. V., “Matematicheskaya model prostranstvennogo kodirovaniya v gippokampalnoi formatsii. II. Neirodinamicheskie korrelyaty mentalnykh traektorii i problema prinyatiya reshenii”, Matematicheskaya biologiya i bioinformatika, 9:1 (2014), 216–256 | DOI

[29] Tsukerman V. D., “K tvorcheskomu poznaniyu kreativnye nachala relyatsionnykh neironnykh setei s chetnym tsiklicheskim tormozheniem”, Trudy VII Vserossiiskoi konferentsii. Nelineinaya dinamika v kognitivnykh issledovaniyakh 2021 (Nizhnii Novgorod, 20–24 sentyabrya 2021), IPFRAN, Nizhnii Novgorod, 2021, 186–189

[30] Mur-Artal R., Montiel J. M. M., Tardós J. D., “ORB-SLAM: A Versatile and Accurate Monocular SLAM System”, IEEE Transactions on Robotics, 31:5 (2015), 1147–1163 | DOI

[31] Quigley M., Conley K., Gerkey B., Faust J., Foote T., Leibs J., Wheeler R., Ng A. Y., “ROS: an open-source Robot Operating System”, ICRA Workshop on Open Source Software, 3 (2009), 5

[32] Ball D., Heath S., Wiles J., Wyeth G., Corke P., Milford M., “OpenRatSLAM: an open source brain-based SLAM system”, Autonomous Robots, 34 (2013), 149–176 | DOI

[33] Samsonovich A., McNaughton B. L., “Path integration and cognitive mapping in a continuous attractor neural network model”, The Journal of Neuroscience, 17 (1997), 5900–5920 | DOI

[34] Rublee E., Rabaud V., Konolige K., Bradski G. R., “ORB: An efficient alternative to SIFT or SURF”, IEEE International Conference on Computer Vision, ICCV 2011 (Barcelona, Spain, November 6–13, 2011), 2564–2571

[35] Triggs B., McLauchlan P. F., Hartley R. I., Fitzgibbon A. W., “Bundle Adjustment — A Modern Synthesis”, Vision Algorithms: Theory and Practice. IWVA 1999, Lecture Notes in Computer Science, 1883, eds. Triggs, B., Zisserman, A., Szeliski, R., Springer, Berlin, Heidelberg, 2000, 298–372 | DOI

[36] Galvez-López D., Tardos J. D., “Bags of Binary Words for Fast Place Recognition in Image Sequences”, IEEE Transactions on Robotics, 28 (2012), 1188–1197 | DOI

[37] Horn B. K. P., “Closed-form solution of absolute orientation using unit quaternions”, Journal of the Optical Society of America A, 4 (1987), 629–641 | DOI

[38] Geiger A., Lenz P., Stiller C., and Urtasun R., “Vision meets robotics: the KITTI dataset”, The International Journal of Robotics Research, 32 (2013), 1231–1237 | DOI

[39] Yu F., Wu Y., Ma S., Xu M., Li H., Qu H., Song C., Wang T., Zhao R., Shi L., “Brain-inspired multimodal hybrid neural network for robot place recognition”, Science RoboticS, 8 (2023) | DOI

[40] Zhang H., Rich P. D., Lee A. K., Sharpee T. O., “Hippocampal spatial representations exhibit a hyperbolic geometry that expands with experience”, Nature Neuroscience, 26 (2023), 131–139 | DOI