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Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence

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Abstract

Uncovering how mental representations acquire, recall, and decode spatial information about relative locations and environmental attributes (cognitive map) involves different challenges. This work is geared towards theoretical discussions on the controversial issue of cognitive scalability for understanding cognitive map emergence from place and grid cells at the intersection between neuroscience and artificial intelligence. In our view, different place maps emerge from parallel and hierarchical neural structures supporting a global cognitive map. The mechanisms sustaining these maps do not only process sensory input but also assign the input to a location. Contentious issues are presented around these concepts and provide concrete suggestions for moving the field forward. We recommend approaching the described challenges guided by AI-based theoretical aspects of encoded place instead of based chiefly on technological aspects to study the brain. SIGNIFICANCE: A formal difference exists between the concepts of spatial representations between experimental neuroscientists and computer scientists and engineers in the so-called neural-based autonomous navigation field. From a neuroscience perspective, we consider the position of an organism’s body to be entirely determined by translational spatial information (e.g., visited places and velocities). An organism predicts where it is at a specific time using continuous or discrete spatial functions embedded into navigation systems. From these functions, we infer that the concept of place has emerged. However, from an engineering standpoint, we represent structured scaffolds of behavioral processes to determine movements from the organism’s current position to some other spatial locations. These scaffolds are certainly affected by the system’s designer. Therefore, the coding of place, in this case, is predetermined. The contrast between emergent cognitive map through inputs versus predefined spatial recognition between two fields creates an inconsistency. Clarifying this tension can inform us on how the brain encodes abstract knowledge to represent spatial positions, which hints at a universal theory of cognition.

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Data Availability

No data recording or analysis were made for this manuscript.

Notes

  1. https://braininitiative.nih.gov/

  2. https://www.humanbrainproject.eu/en/robots

  3. https://www.ibm.com/watson

  4. https://www.apple.com/siri/

References

  1. Engelbrecht AP. Computational Intelligence: An Introduction: Second Edition. Computational Intelligence: An Introduction: Second Edition. 2007.

  2. Macpherson T, Churchland A, Sejnowski T, DiCarlo J, Kamitani Y, Takahashi H, et al. Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks. 2021.

  3. Hong G, Lieber CM. Novel electrode technologies for neural recordings. Nat Rev Neurosci. 2019.

  4. Weisenburger S, Vaziri A. A guide to emerging technologies for large-scale and whole-brain optical imaging of neuronal activity. Ann Rev Neurosci. 2018.

  5. Friston KJ. Modalities, modes, and models in functional neuroimaging. Science. 2009.

  6. Josselyn SA, Tonegawa S. Memory engrams: Recalling the past and imagining the future. Science. 2020.

  7. Lerner TN, Ye L, Deisseroth K. Communication in Neural Circuits: Tools, Opportunities, and Challenges. Cell. 2016.

  8. Buzsáki G. Large-scale recording of neuronal ensembles. Nat Neurosci. 2004.

  9. Urai AE, Doiron B, Leifer AM, Churchland AK. Large-scale neural recordings call for new insights to link brain and behavior. Nat Neurosci. 2022.

  10. Hawkins J, Lewis M, Klukas M, Purdy S, Ahmad S. A framework for intelligence and cortical function based on grid cells in the neocortex. Front Neural Circuits. 2019.

  11. O’Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 1971.

  12. O’Keefe J, Nadel L. Hippocampus as cognitive map. Behav Brain Sci. 1979.

  13. Wu X, Zheng Z, Weng J. On Machine Thinking. In: Proceedings of the International Joint Conference on Neural Networks. 2021.

  14. O’Keefe J, Krupic J. Do hippocampal pyramidal cells respond to nonspatial stimuli? Physiol Rev. 2021.

  15. Dong C, Madar AD, Sheffield MEJ. Distinct place cell dynamics in CA1 and CA3 encode experience in new environments. Nat Commun. 2021.

  16. Moser EI, Kropff E, Moser MB. Place cells, grid cells, and the brain’s spatial representation system. Ann Rev Neurosci. 2008.

  17. Hafting T, Fyhn M, Molden S, Moser MB, Moser EI. Microstructure of a spatial map in the entorhinal cortex. Nature. 2005.

  18. Fyhn M, Molden S, Witter MP, Moser EI, Moser MB. Spatial representation in the entorhinal cortex. Science. (80);2004.

  19. Sorscher B, Mel GC, Ganguli S, Ocko SA. A unified theory for the origin of grid cells through the lens of pattern formation. In: Advances in Neural Information Processing Systems. 2019.

  20. Bush D, Barry C, Manson D, Burgess N. Using Grid Cells for Navigation. Neuron. 2015.

  21. McNaughton BL, Battaglia FP, Jensen O, Moser EI, Moser MB. Path integration and the neural basis of the “cognitive map.” Nat Rev Neurosci. 2006.

  22. Erdem UM, Hasselmo M. A goal-directed spatial navigation model using forward trajectory planning based on grid cells. Eur J Neurosci. 2012.

  23. Gardner RJ, Hermansen E, Pachitariu M, Burak Y, Baas NA, Dunn BA, et al. Toroidal topology of population activity in grid cells. Nature. 2022.

  24. Jacobs LF, Schenk F. Unpacking the Cognitive Map: The Parallel Map Theory of Hippocampal Function. Psychological Review. 2003.

  25. Jacobs LF. The evolution of the cognitive map. In: Brain, Behavior and Evolution. 2003.

  26. Redish a. D. Beyond the cognitive map: from place cells to episodic memory. Cambridge, MA MIT Press. 1999.

  27. Arkin RC. Behaviour-Based Robotics. Robotics. 1998.

  28. Guanella A, Kiper D, Verschure P. A model of grid cells based on a twisted torus topology. In: Int J Neural Sys. 2007.

  29. Santos-Pata D, Zucca R, Low SC, Verschure PFMJ. Size matters: How scaling affects the interaction between grid and border cells. Front Comput Neurosci. 2017.

  30. Bonnevie T, Dunn B, Fyhn M, Hafting T, Derdikman D, Kubie JL, et al. Grid cells require excitatory drive from the hippocampus. Nat Neurosci. 2013.

  31. Zhao R, Grunke SD, Keralapurath MM, Yetman MJ, Lam A, Lee TC, et al. Impaired Recall of Positional Memory following Chemogenetic Disruption of Place Field Stability. Cell Rep. 2016.

  32. Henriksen EJ, Colgin LL, Barnes CA, Witter MP, Moser MB, Moser EI. Spatial representation along the proximodistal axis of CA1. Neuron. 2010.

  33. Zhang SJ, Ye J, Miao C, Tsao A, Cerniauskas I, Ledergerber D, et al. Optogenetic dissection of entorhinal-hippocampal functional connectivity. Science. (80);2013.

  34. Lu L, Leutgeb JK, Tsao A, Henriksen EJ, Leutgeb S, Barnes CA, et al. Impaired hippocampal rate coding after lesions of the lateral entorhinal cortex. Nat Neurosci. 2013.

  35. Deadwyler SA, West JR, Cotman CW, Lynch G. Physiological studies of the reciprocal connections between the hippocampus and entorhinal cortex. Exp Neurol. 1975.

  36. Lian Y, Burkitt AN. Learning an efficient hippocampal place map from entorhinal inputs using non-negative sparse coding. eNeuro. 2021.

  37. Dordek Y, Soudry D, Meir R, Derdikman D. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis. Elife. 2016.

  38. Edvardsen V. Goal-directed navigation based on path integration and decoding of grid cells in an artificial neural network. Nat Comput. 2019.

  39. Fernandez-Leon JA, Acosta GG, Mayosky MA. Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation. Rob Auton Syst. 2009.

  40. Brooks RA. A Robust Layered Control System For A Mobile Robot. IEEE J Robot Autom. 1986.

  41. Edvardsen V, Bicanski A, Burgess N. Navigating with grid and place cells in cluttered environments. Hippocampus. 2020.

  42. Tommasi L, Thinus-Blanc C. Generalization in Place Learning and Geometry Knowledge in Rats. Learn Mem. 2004.

  43. Krupic J, Bauza M, Burton S, O’Keefe J. Framing the grid: effect of boundaries on grid cells and navigation. J Physiol. 2016.

  44. Bicanski A, Burgess N. Neuronal vector coding in spatial cognition. Nat Rev Neurosci. 2020.

  45. Acosta GG, Curti HJ, Calvo OA. Autonomous underwater pipeline inspection in AUTOTRACKER Project: The navigation module. In: Oceans 2005 - Europe. 2005.

  46. Guanella A, Verschure PFMJ. Prediction of the position of an animal based on populations of grid and place cells: A comparative simulation study. J Integr Neurosci. 2007.

  47. Greeno JG, Moore JL. Situativity and Symbols: Response to Vera and Simon. Cogn Sci. 1993.

  48. Mountcastle VB. Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J Neurophysiol. 1957.

  49. Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat’s striate cortex. J Physiol. 1959.

  50. Hawkins J, Ahmad S, Cui Y. A theory of how columns in the neocortex enable learning the structure of the world. Front Neural Circuits. 2017.

  51. Harris JA, Mihalas S, Hirokawa KE, Whitesell JD, Choi H, Bernard A, et al. Hierarchical organization of cortical and thalamic connectivity. Nature. 2019.

  52. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex. 1991.

  53. Barlow H. Sensory Mechanisms, the Reduction of Redundancy, and Intelligence. NPL Symp Mech Thought Process. 1959.

  54. Földiák P. Forming sparse representations by local anti-Hebbian learning. Biol Cybern. 1990.

  55. Bell AJ, Sejnowski TJ. The “independent components” of natural scenes are edge filters. Vision Res. 1997.

  56. Olshausen BA, Field DJ. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 1996.

  57. Chalk M, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. Proc Natl Acad Sci USA. 2018.

  58. Clayton NS, Salwiczek LH, Dickinson A. Episodic memory. Curr Biol. 2007.

  59. Ocko SA, Hardcastle K, Giocomo LM, Ganguli S. Emergent elasticity in the neural code for space. Proc Natl Acad Sci USA. 2018.

  60. Colgin LL, Moser EI, Moser MB. Understanding memory through hippocampal remapping. Trends in Neurosci. 2008.

  61. Muller RU, Kubie JL. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J Neurosci. 1987.

  62. Minsky M. Steps Toward Artificial Intelligence. Proceedings of the IRE. 1961.

  63. Banino A, Barry C, Uria B, Blundell C, Lillicrap T, Mirowski P, et al. Vector-based navigation using grid-like representations in artificial agents. Nature. 2018.

  64. Andersson SO, Moser EI, Moser MB. Visual stimulus features that elicit activity in object-vector cells. Commun Biol. 2021.

  65. Fuhs MC, Redish AD, Touretzky DS. A Visually Driven Hippocampal Place Cell Model. In: Comp Neurosci. 1998.

  66. O’keefe J, Conway DH. Experimental Brain Research Hippocampal Place Units in the Freely Moving Rat: Why They Fire Where They Fire. Brain Res. 1978.

  67. Høydal ØA, Skytøen ER, Andersson SO, Moser MB, Moser EI. Object-vector coding in the medial entorhinal cortex. Nature. 2019.

  68. Barry C, Lever C, Hayman R, Hartley T, Burton S, O’Keefe J, et al. The boundary vector cell model of place cell firing and spatial memory. Rev Neurosci. 2006.

  69. Lever C, Burton S, Jeewajee A, O’Keefe J, Burgess N. Boundary vector cells in the subiculum of the hippocampal formation. J Neurosci. 2009.

  70. O’Keefe J, Recce ML. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus. 1993.

  71. Burgess N, O’Keefe J. Models of place and grid cell firing and theta rhythmicity. Curr Opin Neurobiol. 2011.

  72. Jercog PE, Ahmadian Y, Woodruff C, Deb-Sen R, Abbott LF, Kandel ER. Heading direction with respect to a reference point modulates place-cell activity. Nat Commun. 2019.

  73. Wang Y, Xu X, Wang R. An energy model of place cell network in three dimensional space. Front Neurosci. 2018.

  74. Sarel A, Finkelstein A, Las L, Ulanovsky N. Vectorial representation of spatial goals in the hippocampus of bats. Science. 2017;(80).

  75. LaChance PA, Todd TP, Taube JS. A sense of space in postrhinal cortex. Science. 2019;(80).

  76. Boccara CN, Sargolini F, Thoresen VH, Solstad T, Witter MP, Moser EI, et al. Grid cells in pre-and parasubiculum. Nat Neurosci. 2010.

  77. Taube JS, Muller RU, Ranck JB. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci. 1990.

  78. Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, Moser MB, et al. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science. 2006;(80).

  79. Kropff E, Carmichael JE, Moser MB, Moser EI. Speed cells in the medial entorhinal cortex. Nature. 2015.

  80. Kropff E, Carmichael JE, Moser EI, Moser MB. Frequency of theta rhythm is controlled by acceleration, but not speed, in running rats. Neuron. 2021.

  81. Solstad T, Boccara CN, Kropff E, Moser MB, Moser EI. Representation of geometric borders in the entorhinal cortex. Science. 2008;(80).

  82. Moser EI, Moser MB. Hippocampus and Neural Representations. In: Encyclopedia of Neuroscience. 2009.

  83. Touretzky DS, Redish AD. Theory of rodent navigation based on interacting representations of space. Hippocampus. 1996.

  84. Burgess N, Recce M, O’Keefe J. A model of hippocampal function. Neural Networks. 1994.

  85. Jeffery KJ, Anderson MI, Hayman R, Chakraborty S. A proposed architecture for the neural representation of spatial context. Neurosci Biobehav Rev. 2004.

  86. Dayan P, Abbott LF. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Comp Mathematical Model Neural. 2001.

  87. Wiskott L, Sejnowski TJ. Slow feature analysis: Unsupervised learning of invariances. Neural Comput. 2002.

  88. Cepelewicz J. The brain maps out ideas and memories like spaces. Quanta. 2019.

  89. Roe AW. Columnar connectome: Toward a mathematics of brain function. Netw Neurosci. 2019.

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Acknowledgements

We thank Ahmet Kerim Uysal for comments on the final manuscript.

Funding

JAF is supported by The National Scientific and Technical Research Council (CONICET), Argentina, through the Scientific and Technological Researcher Career program (DI-2019–2516-APN-GRH-CONICET).

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Authors and Affiliations

  1. Exact Sciences Faculty-UNCPBA, CIFICEN (UNCPBA-CICPBA-CONICET) and INTIA (UNCPBA-CICPBA), Tandil, Argentina

    Jose A. Fernandez-Leon

  2. National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Jose A. Fernandez-Leon & Gerardo G. Acosta

  3. Engineering Faculty-UNCPBA, INTELYMEC-CIFICEN (UNCPBA-CICPBA-CONICET), Olavarría, Argentina

    Gerardo G. Acosta

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  1. Jose A. Fernandez-Leon
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JAF-L: Conceptualization, Investigation, Methodology, Writing—original and final draft, Writing—review and editing; GGA: Writing—final draft, Writing—review and editing.

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Correspondence to Jose A. Fernandez-Leon.

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Fernandez-Leon, J.A., Acosta, G.G. Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence. Cogn Comput 16, 2334–2344 (2024). https://doi.org/10.1007/s12559-022-10064-w

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