Place Cells, Grid Cells, and the Brain's Spatial Representation System
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Grid Cells Are Ubiquitous in Neural Networks
Grid Cells Are Ubiquitous in Neural Networks Songlin Li1†, Yangdong Deng2*†, Zhihua Wang1 1 Department of Microelectronics and Nanoelectronics, Tsinghua University 2 School of Software, Tsinghua University * Corresponding author: Email [email protected] † The two authors contributed equally to this work. Abstract: Grid cells are believed to play an important role in both spatial and non-spatial cognition tasks. A recent study observed the emergence of grid cells in an LSTM for path integration. The connection between biological and artificial neural networks underlying the seemingly similarity, as well as the application domain of grid cells in deep neural networks (DNNs), expect further exploration. This work demonstrated that grid cells could be replicated in either pure vision based or vision guided path integration DNNs for navigation under a proper setting of training parameters. We also show that grid-like behaviors arise in feedforward DNNs for non-spatial tasks. Our findings support that the grid coding is an effective representation for both biological and artificial networks. Introduction: It has been long hypothesized that animals use a “cognitive map”1, namely, an internal knowledge representation to support intelligent behaviors. This proposal was later proved by neuroscience experiments on both spatial2,3 and non-spatial4,5 cognition tasks. Grid cells, which exhibit a spatial periodic firing pattern and thus constitute the hexagonal spatial representation as Fyhn et al has observed2, were discovered in the medial entorhinal cortex (MEC) of rodents2,3. Subsequent studies proposed models to characterize the behavior of grid cells in terms of spike-timing correlations6-8. The discovery of grid cells as well as the fact that different grid cells forms a multi-scale representation inspired the theory of vector-based navigation9. -
Spatial View Cells in the Primate Hippocampus
European Journal of Neuroscience, Vol. 9, pp. 1789-1794, 1997 @ European Neuroscience Association SHORT COMMUNICATION Spatial View Cells in the Primate Hippocampus Edmund T. Rolls, Robert G. Robertson and Pierre Georges-FranGois Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK Keywords: hippocampus, memory, place, space, view, Abstract Hippocampal function was analysed by making recordings in rhesus monkeys actively walking in the laboratory. In a sample of 352 cells recorded in the hippocampus and parahippocampal cortex, a population of ‘spatial view’ cells was found to respond when the monkey looked at a part of the environment. The responses of these hippocampal neurons (i) occur to a view of space ‘out there’, not to the place where the monkey is, (ii) depend on where the monkey is looking, as shown by measuring eye position, (iii) do not encode head direction, and (iv) provide a spatial representation that is allocentric, i.e. in world coordinates. This representation of space ‘out there’ would be an appropriate part of a primate memory system involved in memories of where in an environment an object was seen, and more generally in the memory of particular events or episodes, for which a spatial component normally provides part of the context. Damage to the temporal lobe that includes the hippocampal formation implicated, for example a memory of where in space an object has or to one of its main connection pathways, the fornix, produces been seen, which can be remembered perfectly even when the human amnesia (Scoville and Milner, 1957; Gaffan and Gaffan, 1991; Squire or animal has never been to that particular position in space. -
Marine Vehicles Localization Using Grid Cells for Path Integration
Marine Vehicles Localization Using Grid Cells for Path Integration Ignacio Carluchoa, Manuel F. Baileya, Mariano De Paulab, Corina Barbalataa [email protected], [email protected], mariano.depaula@fio.unicen.edu.ar, [email protected] a Department of Mechanical Engineering, Louisiana State University, Baton Rouge, USA b INTELYMEC Group, Centro de Investigaciones en F´ısica e Ingenier´ıa del Centro CIFICEN – UNICEN – CICpBA – CONICET, 7400 Olavarr´ıa, Argentina Abstract—Autonomous Underwater Vehicles (AUVs) are plat- forms used for research and exploration of marine environments. However, these types of vehicles face many challenges that hinder their widespread use in the industry. One of the main limitations is obtaining accurate position estimation, due to the lack of GPS signal underwater. This estimation is usually done with Kalman filters. However, new developments in the neuroscience field have shed light on the mechanisms by which mammals are able to obtain a reliable estimation of their current position based on external and internal motion cues. A new type of neuron, called Grid cells, has been shown to be part of path integration system in the brain. In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles. The model of grid cells used requires only the linear velocities together with heading orientation and provides a reliable estimation of the vehicle’s position. We provide simulation results for an AUV which show the feasibility of our proposed methodology. Index Terms—Grid cells, Navigation, Neuro inspired agents, Autonomous underwater vehicles Fig. 1. A modified BlueROV2 used for experiments I. INTRODUCTION In recent years, the interest in exploring our oceans has that strongly correlates with the animal position in space [5]. -
Position–Theta-Phase Model of Hippocampal Place Cell Activity Applied to Quantification of Running Speed Modulation of Firing Rate
Position–theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate Kathryn McClaina,b,1, David Tingleyb, David J. Heegera,c,1, and György Buzsákia,b,1 aCenter for Neural Science, New York University, New York, NY 10003; bNeuroscience Institute, New York University, New York, NY 10016; and cDepartment of Psychology, New York University, New York, NY 10003 Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved November 18, 2019 (received for review July 24, 2019) Spiking activity of place cells in the hippocampus encodes the Another challenge in understanding this system is the dynamic animal’s position as it moves through an environment. Within a interaction between the rate code and phase code. As these codes cell’s place field, both the firing rate and the phase of spiking in combine, different formats of information are conveyed simulta- the local theta oscillation contain spatial information. We propose a neously in place cell spiking. The interaction can produce unin- – position theta-phase (PTP) model that captures the simultaneous tuitive, although entirely predictable, results in traditional analyses expression of the firing-rate code and theta-phase code in place cell of place cell activity. These practical challenges have hindered the spiking. This model parametrically characterizes place fields to com- pare across cells, time, and conditions; generates realistic place cell effort to explain variability in hippocampal firing rates. A com- simulation data; and conceptualizes a framework for principled hy- putational tool is needed that accounts for the well-established pothesis testing to identify additional features of place cell activity. -
Attractor Cortical Neurodynamics, Schizophrenia, and Depression Edmund T
Rolls Translational Psychiatry (2021) 11:215 https://doi.org/10.1038/s41398-021-01333-7 Translational Psychiatry REVIEW ARTICLE Open Access Attractor cortical neurodynamics, schizophrenia, and depression Edmund T. Rolls 1,2 Abstract The local recurrent collateral connections between cortical neurons provide a basis for attractor neural networks for memory, attention, decision-making, and thereby for many aspects of human behavior. In schizophrenia, a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, contributing to the reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this may reduce the effects of external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition, caused by a reduction of GABA neurotransmission, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. In depression, the lateral orbitofrontal cortex non-reward attractor network system is over-connected and has increased sensitivity to non- reward, providing a new approach to understanding depression. -
Memory System Neurons Represent Gaze Position and The
EXN0010.1177/1179069518787484Journal of Experimental NeuroscienceMeister 787484research-article2018 Journal of Experimental Neuroscience Memory System Neurons Represent Gaze Volume 12: 1–4 © The Author(s) 2018 Position and the Visual World Reprints and permissions: sagepub.co.uk/journalsPermissions.nav Miriam Meister1,2,3 DOI:https://doi.org/10.1177/1179069518787484 10.1177/1179069518787484 1Washington National Primate Research Center, Seattle, WA, USA. 2Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA. 3University of Washington School of Medicine, Seattle, WA, USA. ABSTRACT: The entorhinal cortex, a brain area critical for memory, contains neurons that fire when a rodent is in a certain location (eg, grid cells), or when a monkey looks at certain locations. In rodents, these spatial representations align to visual objects in the environment by firing when the animal is in a preferred location defined by relative position of visual environmental features. Recently, our laboratory found that simultaneously recorded entorhinal neurons in monkeys can exhibit different spatial reference frames for gaze position, including a reference frame of visual environmental features. We also discovered that most of the neurons represent gaze position. These results suggest that gaze information in multiple spatial reference frames is a potent signal used in the primate memory system. Here, I describe how these findings support three underappreciated views of the hippocampal memory system. KEYWORDS: Entorhinal cortex, memory, medial temporal lobe, eye movement, reference frame, hippocampus, primate, gaze RECEIVED: June 8, 2018. ACCEPTED: June 15, 2018. CORRESPONDING AUTHOR: Miriam Meister, Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA. Email: [email protected] TYPE: Commentary COmmENT ON: Meister MLR, Buffalo EA. -
Beta Oscillations and Hippocampal Place Cell Learning During
Beta Oscillations and Hippocampal Place Cell Learning during Exploration of Novel Environments Stephen Grossberg1 1Department of Cognitive and Neural Systems Center for Adaptive Systems, and Center of Excellence for Learning in Education, Science and Technology Boston University 677 Beacon St Boston, Massachusetts 02215 [email protected] Telephone: 617-353-7858/7 Fax: 617-353-7755 http://www.cns.bu.edu/~steve July 29, 2008 Revised: November 24, 2008 CAS/CNS-TR-08-003 Hippocampus, in press Running head: Beta Oscillations during Hippocampal Place Cell Learning Corresponding author: Stephen Grossberg, [email protected] Keywords: grid cells, category learning, spatial navigation, adaptive resonance theory, entorhinal cortex 1 Abstract The functional role of synchronous oscillations in various brain processes has attracted a lot of experimental interest. Berke et al. (2008) reported beta oscillations during the learning of hippocampal place cell receptive fields in novel environments. Such place cell selectivity can develop within seconds to minutes, and can remain stable for months. Paradoxically, beta power was very low during the first lap of exploration, grew to full strength as a mouse traversed a lap for the second and third times, and became low again after the first two minutes of exploration. Beta oscillation power also correlated with the rate at which place cells became spatially selective, and not with theta oscillations. These beta oscillation properties are explained by a neural model of how place cell receptive fields may be learned and stably remembered as spatially selective categories due to feedback interactions between entorhinal cortex and hippocampus. This explanation allows the learning of place cell receptive fields to be understood as a variation of category learning processes that take place in many brain systems, and challenges hippocampal models in which beta oscillations and place cell stability cannot be explained. -
Emergence of Grid-Like Representations by Training
Published as a conference paper at ICLR 2018 EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION Christopher J. Cueva,∗ Xue-Xin Wei∗ Columbia University New York, NY 10027, USA fccueva,[email protected] ABSTRACT Decades of research on the neural code underlying spatial navigation have re- vealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on veloc- ity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. To- gether, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits. 1 INTRODUCTION Understanding the neural code in the brain has long been driven by studying feed-forward -
Models of Grid Cells and Theta Oscillations
BRIEF COMMUNICATIONS ARISING Models of grid cells and theta oscillations ARISING FROM M. M.Yartsev, M. P. Witter & N. Ulanovsky Nature 479, 103–107 (2011) Grid cells recorded in the medial entorhinal cortex (MEC) of freely and in putative velocity-controlled oscillatory inputs identified as inter- moving rodents show a markedly regular spatial firing pattern whose neurons within the septohippocampal circuit7. underlying mechanism has been the subject of intense interest. Yartsev et al.1 recorded the firing of grid cells from bats trained to Yartsev et al.1 report that the firing of grid cells in crawling bats does crawl within the recording environment, a behaviour that they per- not show theta rhythmicity ‘‘causally disproving a major class of form very slowly (a mean speed of 3.7 cm s21 versus 17.6 cm s21 in computational models’’ of grid cell firing that rely on oscillatory our rat data), often stopping entirely (supplementary figure 11 in interference2–7. However, their data may be consistent with these ref. 1). The authors found grid cells with very low firing rates (a mean models, with the apparent lack of theta rhythmicity reflecting slow peak rate of 0.56 Hz versus 5.14 Hz in our data) and little significant movement speeds and low firing rates. Thus, the conclusion of theta modulation. However, matching movement speed is important Yartsev et al. is not supported by their data. for comparisons involving theta. At low speeds movement-related In oscillatory interference models, path integration is performed by theta rhythmicity is strongly attenuated12 and the need for path velocity-dependent variation in the frequencies of theta-band oscilla- integration is reduced. -
Resonating Neurons Stabilize Heterogeneous Grid-Cell Networks
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.10.419200; this version posted December 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Resonating neurons stabilize heterogeneous grid-cell networks 2 3 Divyansh Mittal and Rishikesh Narayanan* 4 5 Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of 6 Science, Bangalore, India. 7 8 *Corresponding Author 9 10 Rishikesh Narayanan, Ph.D. 11 Molecular Biophysics Unit 12 Indian Institute of Science 13 Bangalore 560 012, India. 14 15 e-mail: [email protected] 16 Phone: +91-80-22933372 17 Fax: +91-80-23600535 18 Number of words: 250 (abstract), 120 (significance statement) 19 20 Abbreviated title: Resonating neurons stabilize heterogeneous networks 21 22 Keywords: grid cells, entorhinal cortex, continuous attractor network, resonance, 23 heterogeneities 24 25 Author contributions 26 D. M. and R. N. designed experiments; D. M. performed experiments and carried out data 27 analysis; D. M. and R. N. co-wrote the paper. 28 29 Competing financial interests 30 The authors declare no conflict of interest. 31 32 Acknowledgments 33 This work was supported by the Wellcome Trust-DBT India Alliance (Senior fellowship to 34 RN; IA/S/16/2/502727), Human Frontier Science Program (HFSP) Organization (RN), the 35 Department of Biotechnology through the DBT-IISc partnership program (RN), the Revati 36 and Satya Nadham Atluri Chair Professorship (RN), and the Ministry of Human Resource 37 Development (RN & DM). The authors thank Dr. Poonam Mishra, Dr. -
Models of Grid Cells and Theta Oscillations
Publisher: NPG; Journal: Nature: Nature; Article Type: BCA DOI: 10.1038/nature11276 Models of grid cells and theta oscillations ARISING FROM M. M.Yartsev, M. P. Witter & N. Ulanovsky Nature 479, 103–107 (2011) Grid cells recorded in the medial entorhinal cortex (MEC) of freely moving rodents show a strikingly regular spatial firing pattern whose underlying mechanism has been the subject of intense interest. Yartsev et al.1 report that the firing of grid cells in crawling bats does not show theta rhythmicity “causally disproving a major class of computational models” of grid cell firing that rely on oscillatory interference2–7. However, their data may be consistent with these models, with the apparent lack of theta rhythmicity reflecting slow movement speeds and low firing rates. Thus, Yartsev et al.’s conclusion is not supported by their data. In oscillatory interference models, path integration is performed by velocity- dependent variation in the frequencies of theta-band oscillations, which combine to generate the grid-cell pattern2–4,6,7. In addition, learned associations to environmental sensory inputs (possibly mediated by place cells) ensure that grids are spatially stable over time and are sufficient to maintain firing in familiar environments2,3,8. In rats, the majority of grid cells show theta-modulated firing9,10, and the model predicts specific relationships between modulation frequency, running velocity and grid scale4, which have been verified in grid cells11 and in putative velocity-controlled oscillatory inputs identified as interneurons within the septohippocampal circuit7. Yartsev et al.1 recorded the firing of grid cells from bats trained to crawl within the recording environment, a behaviour that they perform very slowly (a mean speed of 3.7 cm s−1 versus 17.6 cm s−1 in our rat data), often stopping entirely (supplementary figure 11 in ref. -
May-Britt Moser Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Grid Cells, Place Cells and Memory Nobel Lecture, 7 December 2014 by May-Britt Moser Norwegian University of Science and Technology (NTNU), Trondheim, Norway. n 7 December 2014 I gave the most prestigious lecture I have given in O my life—the Nobel Prize Lecture in Medicine or Physiology. Afer lectures by my former mentor John O’Keefe and my close colleague of more than 30 years, Edvard Moser, the audience was still completely engaged, wonderful and responsive. I was so excited to walk out on the stage, and proud to present new and exciting data from our lab. Te title of my talk was: “Grid cells, place cells and memory.” Te long-term vision of my lab is to understand how higher cognitive func- tions are generated by neural activity. At frst glance, this seems like an over- ambitious goal. President Barack Obama expressed our current lack of knowl- edge about the workings of the brain when he announced the Brain Initiative last year. He said: “As humans, we can identify galaxies light years away; we can study particles smaller than an atom. But we still haven’t unlocked the mystery of the three pounds of matter that sits between our ears.” Will these mysteries remain secrets forever, or can we unlock them? What did Obama say when he was elected President? “Yes, we can!” To illustrate that the impossible is possible, I started my lecture by showing a movie with a cute mouse that struggled to bring a biscuit over an edge and home to its nest. Te biscuit was almost bigger than the mouse itself.