Document Type : Full Research Paper


1 Ph.D. Candidate, Cybernetics and Modeling of Biological Systems Lab, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Professor, Cybernetics and Modeling of Biological Systems Lab, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

3 Postdoc Researcher, Cybernetics and Modeling of Biological Systems Lab, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran



In cognition physiology and neuroscience, spatial memory is responsible for the maintenance and recall of information related to environmental details, orientation, and spatial navigation. The brain’s cognitive functions including navigation are executed through correlated and sequential activities of different regions. According to previous research, navigation is largely related to the activities of the Hippocampus (HPC) and the Medial Temporal Lobe (MTL), and retrieval of spatial memories from these regions is controlled by the frontal region and specifically medial prefrontal cortex (mPFC). In this paper we attempt to provide a navigation cognitive model based on computational concepts focusing on bidirectional interaction between HPC and mPFC. This model is provided considering 1. The lack of a comprehensive cognitive model of navigation on a previously learned path and ambiguities regarding the information transferring between the regions, and 2. Disagreement between available models and the currently known actual information flow occurring within the brain. The model is inclusive of the active brain regions engaged in navigation using the cognitive map. Furthermore, we propose a computational model based on van-der-pol neuron pools and controlling rule-base, which is naturally related to the actual brain activity through the synchrony mechanism for information transfer and the mPFC rule-based control of the medial temporal lobe. Finally, by analyzing and presenting evidence, we have shown that the model can be beneficial and practical for describing cognitive and functional disorders in navigation, also for design and prediction of the outcomes of therapeutic and rehabilitation protocols in diseases related to spatial navigation, such as the Alzheimer’s disease.


Main Subjects

  1. Saeidi and F. Towhidkhah, “From grid cells to place cells: A radial basis function network model,” 2008 Cairo Int. Biomed. Eng. Conf. CIBEC 2008, 2008, doi: 10.1109/CIBEC.2008.4786111.
  2. Madl, K. Chen, D. Montaldi, and R. Trappl, “Computational cognitive models of spatial memory in navigation space: A review,” Neural Networks, vol. 65, pp. 18–43, 2015, doi: 10.1016/j.neunet.2015.01.002.
  3. Burgess, E. A. Maguire, and J. O’Keefe, “The human hippocampus and spatial and episodic memory,” Neuron, vol. 35, no. 4, pp. 625–641, 2002, doi: 10.1016/S0896-6273(02)00830-9.
  4. Moscovitch et al., “Functional neuroanatomy of remote episodic, semantic and spatial memory: a unified account based on multiple trace theory.,” J. Anat., vol. 207, no. 1, pp. 35–66, Jul. 2005, doi: 10.1111/j.1469-7580.2005.00421.x.
  5. C. Tolman, “Cognitive maps in rats and men.,” Psychological Review, vol. 55, no. 4. American Psychological Association, US, pp. 189–208, 1948, doi: 10.1037/h0061626.
  6. A. Epstein, E. Z. Patai, J. B. Julian, and H. J. Spiers, “The cognitive map in humans: Spatial navigation and beyond,” Nat. Neurosci., vol. 20, no. 11, pp. 1504–1513, 2017, doi: 10.1038/nn.4656.
  7. Eichenbaum, P. Dudchenko, E. Wood, M. Shapiro, and H. Tanila, “The hippocampus, memory, and place cells: is it spatial memory or a memory space?,” Neuron, vol. 23, no. 2, pp. 209–226, Jun. 1999, doi: 10.1016/s0896-6273(00)80773-4.
  8. Fyhn, S. Molden, M. P. Witter, E. I. Moser, and M.-B. Moser, “Spatial representation in the entorhinal cortex.,” Science, vol. 305, no. 5688, pp. 1258–1264, Aug. 2004, doi: 10.1126/science.1099901.
  9. -B. Moser, D. C. Rowland, and E. I. Moser, “Place cells, grid cells, and memory.,” Cold Spring Harb. Perspect. Biol., vol. 7, no. 2, p. a021808, Feb. 2015, doi: 10.1101/cshperspect.a021808.
  10. O’Keefe and J. Dostrovsky, “The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat.,” Brain Res., vol. 34, no. 1, pp. 171–175, Nov. 1971, doi: 10.1016/0006-8993(71)90358-1.
  11. Hartley, I. Trinkler, and N. Burgess, “Geometric determinants of human spatial memory.,” Cognition, vol. 94, no. 1, pp. 39–75, Nov. 2004, doi: 10.1016/j.cognition.2003.12.001.
  12. R. Preston and H. Eichenbaum, “Interplay of hippocampus and prefrontal cortex in memory,” Curr. Biol., vol. 23, no. 17, pp. 1–21, 2013, doi: 10.1016/j.cub.2013.05.041.
  13. Ungerleider, L. G. , Pessoa, “What and where pathways,” Scholarpedia, vol. 3, p. 5342, 2008.
  14. Jin and S. Maren, “Prefrontal-hippocampal interactions in memory and emotion,” Front. Syst. Neurosci., vol. 9, no. DEC, pp. 1–8, 2015, doi: 10.3389/fnsys.2015.00170.
  15. Hartley et al., “The hippocampus is required for short-term topographical memory in humans,” Hippocampus, vol. 17, no. 1, pp. 34–48, 2007, doi: 10.1002/hipo.20240.
  16. W. Morton, K. R. Sherrill, and A. R. Preston, “Memory integration constructs maps of space, time, and concepts,” Curr. Opin. Behav. Sci., vol. 17, pp. 161–168, 2017, doi: 10.1016/j.cobeha.2017.08.007.
  17. Y. Yu and L. M. Frank, “Hippocampal-cortical interaction in decision making,” Neurobiol. Learn. Mem., vol. 117, pp. 34–41, 2015, doi: 10.1016/j.nlm.2014.02.002.
  18. Place, A. Farovik, M. Brockmann, and H. Eichenbaum, “Bidirectional prefrontal-hippocampal interactions support context-guided memory,” Nat. Neurosci., vol. 19, no. 8, pp. 992–994, 2016, doi: 10.1038/nn.4327.
  19. Jensen and C. D. Tesche, “Frontal theta activity in humans increases with memory load in a working memory task.,” Eur. J. Neurosci., vol. 15, no. 8, pp. 1395–1399, Apr. 2002, doi: 10.1046/j.1460-9568.2002.01975.x.
  20. Chersi and N. Burgess, “The Cognitive Architecture of Spatial Navigation: Hippocampal and Striatal Contributions.,” Neuron, vol. 88, no. 1, pp. 64–77, Oct. 2015, doi: 10.1016/j.neuron.2015.09.021.
  21. Klimesch, R. Freunberger, and P. Sauseng, “Oscillatory mechanisms of process binding in memory.,” Neurosci. Biobehav. Rev., vol. 34, no. 7, pp. 1002–1014, Jun. 2010, doi: 10.1016/j.neubiorev.2009.10.004.
  22. S. Zangbar, T. Ghadiri, M. S. Vafaee, and A. Ebrahimi-kalan, “Theta Oscillations through Hippocampal-Prefrontal Pathway : Importance in Cognitive Performances,” no. April, 2020, doi: 10.1089/brain.2019.0733.
  23. A. Herweg and M. J. Kahana, “Spatial representations in the human brain,” Front. Hum. Neurosci., vol. 12, no. July, pp. 1–16, 2018, doi: 10.3389/fnhum.2018.00297.
  24. L. Phillips, H. A. Robinson, and L. Pozzo-Miller, “Ventral hippocampal projections to the medial prefrontal cortex regulate social memory,” Elife, vol. 8, pp. 1–32, 2019, doi: 10.7554/eLife.44182.
  25. Lithfous, A. Dufour, and O. Després, “Spatial navigation in normal aging and the prodromal stage of Alzheimer’s disease: insights from imaging and behavioral studies,” Ageing Res. Rev., vol. 12, no. 1, p. 201—213, Jan. 2013, doi: 10.1016/j.arr.2012.04.007.
  26. A. Carpenter and S. Grossberg, “Adaptive Resonance Theory,” in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US, 2010, pp. 22–35.
  27. Balanov, N. Janson, D. Postnov, and O. Sosnovtseva, Synchronization: From Simple to Complex (Springer Series in Synergetics). 2008.
  28. Baghdadi, F. Towhidkhah, and R. Rostami, “A mathematical model to mimic the shape of event related desynchronization/synchronization,” J. Theor. Biol., vol. 453, pp. 117–124, 2018, doi: 10.1016/j.jtbi.2018.05.026.
  29. Onken, P. P. C. R. Karunasekara, C. Kayser, and S. Panzeri, “Understanding Neural Population Coding: Information Theoretic Insights from the Auditory System,” Adv. Neurosci., vol. 2014, no. December 2015, pp. 1–14, 2014, doi: 10.1155/2014/907851.
  30. Kumar, S. Rotter, and A. Aertsen, “Spiking activity propagation in neuronal networks: Reconciling different perspectives on neural coding,” Nat. Rev. Neurosci., vol. 11, no. 9, pp. 615–627, 2010, doi: 10.1038/nrn2886.
  31. B. Fritz, M. Elhilali, S. V David, and S. A. Shamma, “Auditory attention—focusing the searchlight on sound,” Curr. Opin. Neurobiol., vol. 17, no. 4, pp. 437–455, 2007, doi:
  32. Jeanne Sholl, “The role of a self-reference system in spatial navigation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 2205, pp. 217–232, 2001.
  33. Wang and E. Spelke, “Human spatial representation: insights from animals.,” Trends Cogn. Sci., vol. 6, no. 9, p. 376, Sep. 2002, doi: 10.1016/s1364-6613(02)01961-7.
  34. Mou, T. P. McNamara, C. M. Valiquette, and B. Rump, “Allocentric and egocentric updating of spatial memories.,” J. Exp. Psychol. Learn. Mem. Cogn., vol. 30, no. 1, pp. 142–157, Jan. 2004, doi: 10.1037/0278-7393.30.1.142.
  35. Hodgson and D. Waller, “Lack of set size effects in spatial updating: Evidence for offline updating.,” J. Exp. Psychol. Learn. Mem. Cogn., vol. 32, no. 4, pp. 854–866, Jul. 2006, doi: 10.1037/0278-7393.32.4.854.
  36. Droulez and A. Berthoz, “A neural network model of sensoritopic maps with predictive short-term memory properties.,” Proc. Natl. Acad. Sci. U. S. A., vol. 88, no. 21, pp. 9653–9657, Nov. 1991, doi: 10.1073/pnas.88.21.9653.
  37. Byrne, S. Becker, and N. Burgess, “Remembering the past and imagining the future: a neural model of spatial memory and imagery.,” Psychol. Rev., vol. 114, no. 2, pp. 340–375, Apr. 2007, doi: 10.1037/0033-295X.114.2.340.
  38. Biology, “Computational Models of Dementia and Neurological Problems Computational Models of Dementia and Neurological Problems,” no. February 2007, 2014, doi: 10.1007/978-1-59745-520-6.
  39. Zou, D. Coyle, K. F. Wong-Lin, and L. Maguire, “Computational study of Hippocampal-septal theta rhythm changes due to Beta-Amyloid-Altered ionic channels,” PLoS One, vol. 6, no. 6, 2011, doi: 10.1371/journal.pone.0021579.
  40. A. Cutsuridis, V. and Moustafa, “No TitlComputational models of Alzheimer’s diseasee,” Scholarpedia, vol. 12, no. 1, p. 32144, 2017.
  41. Lisman, “The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme.,” Hippocampus, vol. 15, no. 7, pp. 913–922, 2005, doi: 10.1002/hipo.20121.