Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation

To guide navigation, the nervous system integrates multisensory self-motion and landmark information. We dissected how these inputs generate spatial representations by recording entorhinal grid, border and speed cells in mice navigating virtual environments. Manipulating the gain between the animal’s locomotion and the visual scene revealed that border cells responded to landmark cues while grid and speed cells responded to combinations of locomotion, optic flow and landmark cues in a context-dependent manner, with optic flow becoming more influential when it was faster than expected. A network model explained these results by revealing a phase transition between two regimes in which grid cells remain coherent with or break away from the landmark reference frame. Moreover, during path-integration-based navigation, mice estimated their position following principles predicted by our recordings. Together, these results provide a theoretical framework for understanding how landmark and self-motion cues combine during navigation to generate spatial representations and guide behavior.Using a combination of in vivo recording and theoretical modeling, Campbell et al. develop a framework for understanding the integration of self-motion and landmark cues in entorhinal cortex and demonstrate that these principles extend to behavior.

[1]  May-Britt Moser,et al.  The entorhinal grid map is discretized , 2012, Nature.

[2]  M. Moser,et al.  Representation of Geometric Borders in the Entorhinal Cortex , 2008, Science.

[3]  John A. King,et al.  How vision and movement combine in the hippocampal place code , 2012, Proceedings of the National Academy of Sciences.

[4]  Edvard I. Moser,et al.  Speed cells in the medial entorhinal cortex , 2015, Nature.

[5]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[6]  Etienne Save,et al.  The retrosplenial cortex is necessary for path integration in the dark , 2014, Behavioural Brain Research.

[7]  Quanxin Wang,et al.  Gateways of Ventral and Dorsal Streams in Mouse Visual Cortex , 2011, The Journal of Neuroscience.

[8]  K. Jeffery,et al.  Grid Cells Form a Global Representation of Connected Environments , 2015, Current Biology.

[9]  K. Jeffery,et al.  Experience-dependent rescaling of entorhinal grids , 2007, Nature Neuroscience.

[10]  Douglas A Nitz,et al.  Retrosplenial cortex maps the conjunction of internal and external spaces , 2015, Nature Neuroscience.

[11]  B L McNaughton,et al.  Path Integration and Cognitive Mapping in a Continuous Attractor Neural Network Model , 1997, The Journal of Neuroscience.

[12]  Bruce L. McNaughton,et al.  Path integration and the neural basis of the 'cognitive map' , 2006, Nature Reviews Neuroscience.

[13]  Bruce L. McNaughton,et al.  A Model of the Neural Basis of the Rat's Sense of Direction , 1994, NIPS.

[14]  Gerit Pfuhl,et al.  Functional Split between Parietal and Entorhinal Cortices in the Rat , 2012, Neuron.

[15]  J. O’Keefe,et al.  How environment geometry affects grid cell symmetry and what we can learn from it , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Torkel Hafting,et al.  Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex , 2006, Science.

[17]  Caswell Barry,et al.  Grid cell symmetry is shaped by environmental geometry , 2015, Nature.

[18]  John O'Keefe,et al.  Local transformations of the hippocampal cognitive map , 2018, Science.

[19]  D. Tank,et al.  Membrane potential dynamics of grid cells , 2013, Nature.

[20]  Thomas J. Wills,et al.  Development of the Hippocampal Cognitive Map in Preweanling Rats , 2010, Science.

[21]  Kenneth D. Harris,et al.  High-Dimensional Cluster Analysis with the Masked EM Algorithm , 2013, Neural Computation.

[22]  J. O’Keefe,et al.  Grid cells and theta as oscillatory interference: Electrophysiological data from freely moving rats , 2008, Hippocampus.

[23]  Menno P. Witter,et al.  Excitatory Postrhinal Projections to Principal Cells in the Medial Entorhinal Cortex , 2015, The Journal of Neuroscience.

[24]  Edvard I. Moser,et al.  Shearing-induced asymmetry in entorhinal grid cells , 2015, Nature.

[25]  James H. Marshel,et al.  Functional Specialization of Seven Mouse Visual Cortical Areas , 2011, Neuron.

[26]  S. Strogatz From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators , 2000 .

[27]  D. Tank,et al.  Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice , 2007, Neuron.

[28]  Christopher D. Harvey,et al.  Choice-specific sequences in parietal cortex during a virtual-navigation decision task , 2012, Nature.

[29]  Johannes C. Dahmen,et al.  Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex , 2015, Nature Neuroscience.

[30]  B. J. Clark,et al.  Disruption of the head direction cell network impairs the parahippocampal grid cell signal , 2015, Science.

[31]  Thomas J. Wills,et al.  Absence of Visual Input Results in the Disruption of Grid Cell Firing in the Mouse , 2016, Current Biology.

[32]  Yoram Burak,et al.  Accurate Path Integration in Continuous Attractor Network Models of Grid Cells , 2008, PLoS Comput. Biol..

[33]  Yasser Roudi,et al.  Ten Years of Grid Cells. , 2016, Annual review of neuroscience.

[34]  Lisa M. Giocomo,et al.  Grid Cells Use HCN1 Channels for Spatial Scaling , 2011, Cell.

[35]  D. Amaral,et al.  Cortical afferents of the perirhinal, postrhinal, and entorhinal cortices of the rat , 1998 .

[36]  George Paxinos,et al.  The Mouse Brain in Stereotaxic Coordinates , 2001 .

[37]  M. Carandini,et al.  Integration of visual motion and locomotion in mouse visual cortex , 2013, Nature Neuroscience.

[38]  B. J. Clark,et al.  Passive Transport Disrupts Grid Signals in the Parahippocampal Cortex , 2015, Current Biology.

[39]  Lisa M. Giocomo,et al.  Hyperpolarization‐activated cyclic nucleotide‐gated 1 independent grid cell‐phase precession in mice , 2014, Hippocampus.

[40]  Olga Kornienko,et al.  Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex , 2016, eLife.

[41]  S. Remy,et al.  Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections , 2016, Nature Neuroscience.

[42]  Surya Ganguli,et al.  Environmental Boundaries as an Error Correction Mechanism for Grid Cells , 2015, Neuron.

[43]  Dora E Angelaki,et al.  How Optic Flow and Inertial Cues Improve Motion Perception. , 2014, Cold Spring Harbor symposia on quantitative biology.

[44]  Jonathan R. Whitlock,et al.  Fragmentation of grid cell maps in a multicompartment environment , 2009, Nature Neuroscience.

[45]  KiJung Yoon,et al.  Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice , 2016, Neuron.

[46]  Edvard I Moser,et al.  Development of the Spatial Representation System in the Rat , 2010, Science.

[47]  Mark C. Fuhs,et al.  A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex , 2006, The Journal of Neuroscience.

[48]  H. Teitelbaum,et al.  Relationship between hippocampal theta activity and running speed in the rat. , 1975, Journal of comparative and physiological psychology.

[49]  Demetris K. Roumis,et al.  Functional Specialization of Mouse Higher Visual Cortical Areas , 2011, Neuron.

[50]  Michael E. Hasselmo,et al.  Multiple Running Speed Signals in Medial Entorhinal Cortex , 2016, Neuron.

[51]  R U Muller,et al.  Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[52]  T. Hafting,et al.  Microstructure of a spatial map in the entorhinal cortex , 2005, Nature.

[53]  Surya Ganguli,et al.  A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex , 2017, Neuron.

[54]  Surya Ganguli,et al.  Emergent Elasticity in the Neural Code for Space , 2018 .

[55]  Dmitriy Aronov,et al.  Engagement of Neural Circuits Underlying 2D Spatial Navigation in a Rodent Virtual Reality System , 2014, Neuron.