دورية أكاديمية

Toroidal topology of population activity in grid cells.

التفاصيل البيبلوغرافية
العنوان: Toroidal topology of population activity in grid cells.
المؤلفون: Gardner RJ; Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway. richard.gardner@ntnu.no., Hermansen E; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway., Pachitariu M; HHMI Janelia Research Campus, Ashburn, VA, USA., Burak Y; Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.; Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel., Baas NA; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. nils.baas@ntnu.no., Dunn BA; Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway. benjamin.dunn@ntnu.no.; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. benjamin.dunn@ntnu.no., Moser MB; Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway., Moser EI; Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway. edvard.moser@ntnu.no.
المصدر: Nature [Nature] 2022 Feb; Vol. 602 (7895), pp. 123-128. Date of Electronic Publication: 2022 Jan 12.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: MEDLINE
أسماء مطبوعة: Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.
مواضيع طبية MeSH: Models, Neurological*, Grid Cells/*physiology, Action Potentials ; Animals ; Entorhinal Cortex/anatomy & histology ; Entorhinal Cortex/cytology ; Entorhinal Cortex/physiology ; Grid Cells/classification ; Male ; Rats ; Rats, Long-Evans ; Sleep/physiology ; Space Perception/physiology ; Wakefulness/physiology
مستخلص: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment 1 . Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations 2 , and are organized in modules 3 that collectively form a population code for the animal's allocentric position 1 . The invariance of the correlation structure of this population code across environments 4,5 and behavioural states 6,7 , independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern 1,8-11 . However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models 12 . This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.
(© 2022. The Author(s).)
References: McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M.-B. Path integration and the neural basis of the ‘cognitive map’. Nat. Rev. Neurosci. 7, 663–678 (2006). (PMID: 1685839410.1038/nrn1932)
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005). (PMID: 1596546310.1038/nature03721)
Stensola, H. et al. The entorhinal grid map is discretized. Nature 492, 72–78 (2012). (PMID: 2322261010.1038/nature11649)
Fyhn, M., Hafting, T., Treves, A., Moser, M.-B. & Moser, E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007). (PMID: 1732290210.1038/nature05601)
Yoon, K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat. Neurosci. 16, 1077–1084 (2013). (PMID: 23852111379751310.1038/nn.3450)
Gardner, R. J., Lu, L., Wernle, T., Moser, M.-B. & Moser, E. I. Correlation structure of grid cells is preserved during sleep. Nat. Neurosci. 22, 598–608 (2019). (PMID: 3091118510.1038/s41593-019-0360-0)
Trettel, S. G., Trimper, J. B., Hwaun, E., Fiete, I. R. & Colgin, L. L. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nat. Neurosci. 22, 609–617 (2019). (PMID: 30911183741205910.1038/s41593-019-0359-6)
Fuhs, M. C. & Touretzky, D. S. A spin glass model of path integration in rat medial entorhinal cortex. J. Neurosci. 26, 4266–4276 (2006). (PMID: 16624947667400710.1523/JNEUROSCI.4353-05.2006)
Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5, e1000291 (2009). (PMID: 19229307263274110.1371/journal.pcbi.1000291)
Guanella, A., Kiper, D. & Verschure, P. A model of grid cells based on a twisted torus topology. Int. J. Neural Syst. 17, 231–240 (2007). (PMID: 1769628810.1142/S0129065707001093)
Couey, J. J. et al. Recurrent inhibitory circuitry as a mechanism for grid formation. Nat. Neurosci. 16, 318–324 (2013). (PMID: 2333458010.1038/nn.3310)
Kropff, E. & Treves, A. The emergence of grid cells: intelligent design or just adaptation? Hippocampus 18, 1256–1269 (2008). (PMID: 1902126110.1002/hipo.20520)
Amari, S. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27, 77–87 (1977). (PMID: 91193110.1007/BF00337259)
Ben-Yishai, R., Bar-Or, R. L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995). (PMID: 77319934205810.1073/pnas.92.9.3844)
Seung, H. S. How the brain keeps the eyes still. Proc. Natl Acad. Sci. USA 93, 13339–13344 (1996). (PMID: 89175922409410.1073/pnas.93.23.13339)
Taube, J. S., Muller, R. U. & Ranck, J. B. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J. Neurosci. 10, 420–435 (1990). (PMID: 2303851657015110.1523/JNEUROSCI.10-02-00420.1990)
Skaggs, W. E., Knierim, J. J., Kudrimoti, H. S. & McNaughton, B. L. A model of the neural basis of the rat’s sense of direction. Adv. Neural Inf. Process. Syst. 7, 173–180 (1995). (PMID: 11539168)
Redish, A. D., Elga, A. N. & Touretzky, D. S. A coupled attractor model of the rodent head direction system. Network 7, 671–685 (1996). (PMID: 10.1088/0954-898X_7_4_004)
Zhang, K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J. Neurosci. 16, 2112–2126 (1996). (PMID: 8604055657851210.1523/JNEUROSCI.16-06-02112.1996)
Yoganarasimha, D., Yu, X. & Knierim, J. J. Head direction cell representations maintain internal coherence during conflicting proximal and distal cue rotations: comparison with hippocampal place cells. J. Neurosci. 26, 622–631 (2006). (PMID: 16407560138818910.1523/JNEUROSCI.3885-05.2006)
Peyrache, A., Lacroix, M. M., Petersen, P. C. & Buzsáki, G. Internally organized mechanisms of the head direction sense. Nat. Neurosci. 18, 569–575 (2015). (PMID: 25730672437655710.1038/nn.3968)
Rybakken, E., Baas, N. & Dunn, B. Decoding of neural data using cohomological feature extraction. Neural Comput. 31, 68–93 (2019). (PMID: 3046258210.1162/neco_a_01150)
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A. & Fiete, I. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat. Neurosci. 22, 1512–1520 (2019). (PMID: 3140636510.1038/s41593-019-0460-x)
Rubin, A. et al. Revealing neural correlates of behavior without behavioral measurements. Nat. Commun. 10, 4745 (2019). (PMID: 31628322680218410.1038/s41467-019-12724-2)
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017). (PMID: 29120427595520610.1038/nature24636)
Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021). (PMID: 33859006824481010.1126/science.abf4588)
Kang, L., Xu, B. & Morozov, D. Evaluating state space discovery by persistent cohomology in the spatial representation system. Front. Comput. Neurosci. 15, 616748 (2021). (PMID: 33897395806044710.3389/fncom.2021.616748)
Sargolini, F. et al. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science 312, 758–762 (2006). (PMID: 1667570410.1126/science.1125572)
Barry, C., Hayman, R., Burgess, N. & Jeffery, K. J. Experience-dependent rescaling of entorhinal grids. Nat. Neurosci. 10, 682–684 (2007). (PMID: 1748610210.1038/nn1905)
Stensola, T., Stensola, H., Moser, M.-B. & Moser, E. I. Shearing-induced asymmetry in entorhinal grid cells. Nature 518, 207–212 (2015). (PMID: 2567341410.1038/nature14151)
Krupic, J., Bauza, M., Burton, S., Barry, C. & O’Keefe, J. Grid cell symmetry is shaped by environmental geometry. Nature 518, 232–235 (2015). (PMID: 25673417457673410.1038/nature14153)
Butler, W. N., Hardcastle, K. & Giocomo, L. M. Remembered reward locations restructure entorhinal spatial maps. Science 363, 1447–1452 (2019). (PMID: 30923222651675210.1126/science.aav5297)
Boccara, C. N., Nardin, M., Stella, F., O’Neill, J. & Csicsvari, J. The entorhinal cognitive map is attracted to goals. Science 363, 1443–1447 (2019). (PMID: 3092322110.1126/science.aav4837)
Latuske, P., Toader, O. & Allen, K. Interspike intervals reveal functionally distinct cell populations in the medial entorhinal cortex. J. Neurosci. 35, 10963–10976 (2015). (PMID: 26245960660527610.1523/JNEUROSCI.0276-15.2015)
Newman, E. L. & Hasselmo, M. E. Grid cell firing properties vary as a function of theta phase locking preferences in the rat medial entorhinal cortex. Front. Syst. Neurosci. 8, 193 (2014). (PMID: 25352787419651910.3389/fnsys.2014.00193)
Csordás, D. É., Fischer, C., Nagele, J., Stemmler, M. & Herz, A. V. M. Spike afterpotentials shape the in vivo burst activity of principal cells in medial entorhinal cortex. J. Neurosci. 40, 4512–4524 (2020). (PMID: 32332120727586710.1523/JNEUROSCI.2569-19.2020)
Finkelstein, A. et al. Three-dimensional head-direction coding in the bat brain. Nature 517, 159–164 (2015). (PMID: 2547005510.1038/nature14031)
Ginosar, G. et al. Locally ordered representation of 3D space in the entorhinal cortex. Nature 596, 404–409 (2021). (PMID: 3438121110.1038/s41586-021-03783-x)
Sussillo, D. & Barak, O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25, 626–649 (2013). (PMID: 2327292210.1162/NECO_a_00409)
Sorscher, B., Mel, G. C., Ocko, S. A., Giocomo, L. & Ganguli, S. A unified theory for the computational and mechanistic origins of grid cells. Preprint at https://doi.org/10.1101/2020.12.29.424583 (2020).
Darshan, R. & Rivkind, A. Learning to represent continuous variables in heterogeneous neural networks. Preprint at https://doi.org/10.1101/2021.06.01.446635 (2021).
Skaggs, W., Mcnaughton, B. & Gothard, K. An information-theoretic approach to deciphering the hippocampal code. Adv. Neural Inf. Process. Syst. 5, 1030–1037 (1992).
McInnes, L., Healy, J. & Melville, J. UMAP: Uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
McInnes, L. UMAP https://github.com/lmcinnes/umap .
Lee, A. & Wilson, M. A. Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36, 1183–1194 (2002). (PMID: 1249563110.1016/S0896-6273(02)01096-6)
Montgomery, S. M., Sirota, A. & Buzsáki, G. Theta and gamma coordination of hippocampal networks during waking and rapid eye movement sleep. J. Neurosci. 28, 6731–6741 (2008). (PMID: 18579747259697810.1523/JNEUROSCI.1227-08.2008)
Meehan, C., Ebrahimian, J., Moore, W. & Meehan, S. Uniform Manifold Approximation and Projection (UMAP) https://www.mathworks.com/matlabcentral/fileexchange/71902 (MATLAB, 2021).
Bellman, R. Dynamic Programming (Princeton Univ. Press, 1957).
Kloke, J. & Carlsson, G. Topological de-noising: strengthening the topological signal. Preprint at https://arxiv.org/abs/0910.5947 (2009).
Spivak, D. I. Metric realization of fuzzy simplicial sets https://www.semanticscholar.org/paper/metric-realization-of-fuzzy-simplicial-sets-spivak/a73fb9d562a3850611d2615ac22c3a8687fa745e (Semantic Scholar, 2009).
Tralie, C., Saul, N. & Bar-On, R. Ripser. py: a lean persistent homology library for python. J. Open Source Softw. 3, 925 (2018). (PMID: 10.21105/joss.00925)
Bauer, U. Ripser: efficient computation of Vietoris-Rips persistence barcodes. J Appl. Comput. Topol. 5, 391–423 (2021). (PMID: 10.1007/s41468-021-00071-5)
Kobak, D. & Linderman, G. C. Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nat. Biotechnol. 39, 156–157 (2021). (PMID: 3352694510.1038/s41587-020-00809-z)
Singh, G. et al. Topological analysis of population activity in visual cortex. J. Vis. 8, 1–18 (2008). (PMID: 1914623910.1167/8.8.11)
Giusti, C., Pastalkova, E., Curto, C. & Itskov, V. Clique topology reveals intrinsic geometric structure in neural correlations. Proc. Natl Acad. Sci. USA 112, 13455–13460 (2015). (PMID: 26487684464078510.1073/pnas.1506407112)
Dabaghian, Y., Mémoli, F., Frank, L. & Carlsson, G. A topological paradigm for hippocampal spatial map formation using persistent homology. PLoS Comput. Biol. 8, e1002581 (2012). (PMID: 22912564341541710.1371/journal.pcbi.1002581)
Spreemann, G., Dunn, B., Botnan, M. B. & Baas, N. A. Using persistent homology to reveal hidden covariates in systems governed by the kinetic Ising model. Phys. Rev. E 97, 032313 (2018). (PMID: 2977611710.1103/PhysRevE.97.032313)
Baas, N. A. On the concept of space in neuroscience. Curr. Opin. Syst. Biol. 1, 32–37 (2017). (PMID: 10.1016/j.coisb.2016.12.002)
De Silva, V., Morozov, D. & Vejdemo-Johansson, M. Persistent cohomology and circular coordinates. Discrete Comput. Geom. 45, 737–759 (2011). (PMID: 10.1007/s00454-011-9344-x)
Hatcher, A. Algebraic Topology (Cambridge University Press, 2002).
Perea, J. A. in Topological Data Analysis: The Abel Symposium 2018 (eds Baas, N. A. et al.) 435–458 (Springer, 2020).
Ledergerber, D. et al. Task-dependent mixed selectivity in the subiculum. Cell Rep. 35, 109175 (2021). (PMID: 34038726817037010.1016/j.celrep.2021.109175)
The GUDHI Project. GUDHI User and Reference Manual (GUDHI Editorial Board, 2021).
Chazal, F., Guibas, L. J., Oudot, S. Y. & Skraba, P. Persistence-based clustering in Riemannian manifolds. J. ACM 60, 41 (2013). (PMID: 10.1145/2535927)
Santos Pata, D. Grid Cells https://github.com/DiogoSantosPata/gridcells (2020).
Själander, M., Jahre, M., Tufte, G. & Reissmann, N. EPIC: an energy-efficient, high-performance GPGPU computing research infrastructure. Preprint at https://arxiv.org/abs/1912.05848 (2020).
Seelig, J. D. & Jayaraman, V. Neural dynamics for landmark orientation and angular path integration. Nature 521, 186–191 (2015). (PMID: 25971509470479210.1038/nature14446)
Kim, S. S., Rouault, H., Druckmann, S. & Jayaraman, V. Ring attractor dynamics in the Drosophila central brain. Science 356, 849–853 (2017). (PMID: 2847363910.1126/science.aal4835)
Green, J. et al. A neural circuit architecture for angular integration in Drosophila. Nature 546, 101–106 (2017). (PMID: 28538731632068410.1038/nature22343)
McNaughton, B. L. et al. Deciphering the hippocampal polyglot: the hippocampus as a path integration system. J. Exp. Biol. 199, 173–185 (1996). (PMID: 857668910.1242/jeb.199.1.173)
Samsonovich, A. & McNaughton, B. L. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17, 5900–5920 (1997). (PMID: 9221787657321910.1523/JNEUROSCI.17-15-05900.1997)
Tsodyks, M. & Sejnowski, T. J. Associative memory and hippocampal place cells. Int. J. Neural Syst. 6, 81–86 (1995).
Aksay, E. et al. Functional dissection of circuitry in a neural integrator. Nat. Neurosci. 10, 494–504 (2007). (PMID: 17369822280311610.1038/nn1877)
Wang, X.-J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008). (PMID: 18957215271029710.1016/j.neuron.2008.09.034)
Machens, C. K., Romo, R. & Brody, C. D. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307, 1121–1124 (2005). (PMID: 1571847410.1126/science.1104171)
Lukashin, A. V. & Georgopoulos, A. P. A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories. Biol. Cybern. 69, 517–524 (1993). (PMID: 827454910.1007/BF01185423)
Romani, S. & Tsodyks, M. Continuous attractors with morphed/correlated maps. PLoS Comput. Biol. 6, e1000869 (2010). (PMID: 20700490291684410.1371/journal.pcbi.1000869)
Compte, A., Brunel, N., Goldman-Rakic, P. S. & Wang, X. J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000). (PMID: 1098275110.1093/cercor/10.9.910)
Wimmer, K., Nykamp, D. Q., Constantinidis, C. & Compte, A. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat. Neurosci. 17, 431–439 (2014). (PMID: 2448723210.1038/nn.3645)
Koyluoglu, O. O., Pertzov, Y., Manohar, S., Husain, M. & Fiete, I. R. Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity. eLife 6, e22225 (2017). (PMID: 28879851577931510.7554/eLife.22225)
Stepanyuk, A. Self-organization of grid fields under supervision of place cells in a neuron model with associative plasticity. Biol. Inspired Cogn. Archit. 13, 48–62 (2015).
Dordek, Y., Soudry, D., Meir, R. & Derdikman, D. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis. eLife 5, e10094 (2016). (PMID: 26952211484178510.7554/eLife.10094)
Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017). (PMID: 2896791010.1038/nn.4650)
D’Albis, T. & Kempter, R. A single-cell spiking model for the origin of grid-cell patterns. PLoS Comput. Biol. 13, e1005782 (2017). (PMID: 28968386563862310.1371/journal.pcbi.1005782)
Monsalve-Mercado, M. M. & Leibold, C. Hippocampal spike-timing correlations lead to hexagonal grid fields. Phys. Rev. Lett. 119, 038101 (2017). (PMID: 2877760610.1103/PhysRevLett.119.038101)
Weber, S. N. & Sprekeler, H. Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity. eLife 7, e34560 (2018). (PMID: 29465399592777210.7554/eLife.34560)
Si, B., Kropff, E. & Treves, A. Grid alignment in entorhinal cortex. Biol. Cybern. 106, 483–506 (2012). (PMID: 2289276110.1007/s00422-012-0513-7)
Langston, R. F. et al. Development of the spatial representation system in the rat. Science 328, 1576–1580 (2010). (PMID: 2055872110.1126/science.1188210)
Wills, T. J., Cacucci, F., Burgess, N. & O’Keefe, J. Development of the hippocampal cognitive map in preweanling rats. Science 328, 1573–1576 (2010). (PMID: 20558720354398510.1126/science.1188224)
Donato, F., Jacobsen, R. I., Moser, M.-B. & Moser, E. I. Stellate cells drive maturation of the entorhinal–hippocampal circuit. Science 355, eaai8178 (2017). (PMID: 2815424110.1126/science.aai8178)
Gray, C. M., Maldonado, P. E., Wilson, M. & McNaughton, B. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods 63, 43–54 (1995). (PMID: 878804710.1016/0165-0270(95)00085-2)
Hardcastle, K., Maheswaranathan, N., Ganguli, S. & Giocomo, L. M. A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron 94, 375–387 (2017). (PMID: 28392071549817410.1016/j.neuron.2017.03.025)
معلومات مُعتمدة: International ERC_ European Research Council
تواريخ الأحداث: Date Created: 20220113 Date Completed: 20220309 Latest Revision: 20221025
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC8810387
DOI: 10.1038/s41586-021-04268-7
PMID: 35022611
قاعدة البيانات: MEDLINE
الوصف
تدمد:1476-4687
DOI:10.1038/s41586-021-04268-7