Progress in brain research, volume 219

Serial Editor Vincent Walsh Institute of Cognitive Neuroscience University College London 17 Queen Square London WC1N 3AR UK Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK 225 Wyman Street, Waltham, MA 02451, USA First edition 2015 # 2015 Elsevier B.V All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein ISBN: 978-0-444-63549-5 ISSN: 0079-6123 For information on all Elsevier publications visit our website at store.elsevier.com Contributors John P Aggleton School of Psychology, Cardiff University, Cardiff, Wales, UK Jean-Christophe Cassel Laboratoire de Neurosciences Cognitives et Adaptatives, UMR 7364, Universite´ de Strasbourg, CNRS, Faculte´ de Psychologie, Neuropoˆle de Strasbourg—GDR 2905 du CNRS, Strasbourg, France Kat Christiansen School of Psychology, Cardiff University, Cardiff, Wales, UK Julie R Dumont Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA Michael E Hasselmo Department of Psychological and Brain Sciences, Center for Memory and Brain, Center for Systems Neuroscience, Graduate Program for Neuroscience, Boston University, Boston, MA, USA Matthew W Jones School of Physiology and Pharmacology, University of Bristol, University Walk, Bristol, UK Laura A Libby Center for Neuroscience, University of California, Davis, CA, USA Sheri J.Y Mizumori Psychology Department, University of Washington, Seattle, WA, USA Andrew J.D Nelson School of Psychology, Cardiff University, Cardiff, UK Anne Pereira de Vasconcelos Laboratoire de Neurosciences Cognitives et Adaptatives, UMR 7364, Universite´ de Strasbourg, CNRS, Faculte´ de Psychologie, Neuropoˆle de Strasbourg—GDR 2905 du CNRS, Strasbourg, France Charan Ranganath Center for Neuroscience, and Department of Psychology, University of California, Davis, CA, USA Maureen Ritchey Center for Neuroscience, University of California, Davis, CA, USA Edmund T Rolls Oxford Centre for Computational Neuroscience, Oxford, and Department of Computer Science, University of Warwick, Coventry, UK v vi Contributors Jeffrey S Taube Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA Valerie L Tryon Psychology Department, University of Washington, Seattle, WA, USA Marian Tsanov Trinity College Institute of Neuroscience, and School of Psychology, Trinity College Dublin, Dublin, Ireland Seralynne D Vann School of Psychology, Cardiff University, Cardiff, UK Robert P Vertes Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA Emilie Werlen School of Physiology and Pharmacology, University of Bristol, University Walk, Bristol, UK Preface The hippocampus is an intriguing and anatomically remarkable structure: it is possessed of a remarkable curvilinear appearance in coronal section, and it is easy to spot in anatomical section with the naked eye in just about any mammalian species A special and important function has been ascribed to it as a result of the pioneering work of John O’Keefe (Nobel Laureate, 2014), who described the remarkable “place cells,” which fire as a function of the location of the rat in the environment Two other important discoveries also give it great importance: longterm potentiation and amnesia Long-term potentiation, the demonstration that synapses are plastic, was first described in the hippocampus by Tim Bliss and Terje Lomo The famous amnesic patient, HM, had a more-or-less complete surgical ablation of the hippocampus Correspondingly, the hippocampus has been implicated in many important neurocognitive functions, with a particular latter-day emphasis on its role in spatial and cognitive mapping, and in declarative (or explicit) memory A substantial body of data suggests that the hippocampal formation plays a critical role in the biological processes underlying at least some forms of memory Sometimes, however, it feels when reading the many, many papers published annually on the hippocampus that it sits apart from the brain, with its functions analyzed in a narrow hippocampo-centric framework—as if the purpose of the rest of the brain is to serve the information processing needs of the hippocampus! This point is made a little facetiously and exaggeratedly, of course Nonetheless, we felt the need to assuage these feelings by assembling this volume to encourage researchers to situate the hippocampus as part of a network connected to the rest of the brain and not to consider it in isolation We therefore present a selection of chapters that concentrate on understanding the functions of the hippocampus in terms of the connectivity of the hippocampus itself: in other words, in terms of its cortical and subcortical inputs and outputs To take just one important illustrative example: the anterior thalamic and rostral thalamic nuclei are abundantly connected with the hippocampal formation and have the capacity to profoundly shape hippocampal spatial and mnemonic information processing, a key point sometimes be overlooked in analyses favoring of hippocampally directed cortical processing We also know that damage to the anterior thalamus results in episodic memory impairment more-or-less similarly severe as that resulting from hippocampal lesions; this may be a function of lost thalamohippocampal information transfer However, the textbooks and the primary literature often heavily emphasize the lessons from patients with hippocampal damage, while neglecting the similarly instructive patients with thalamic damage who also suffer amnesia The complexity of thalamic signals and their contribution to the encoding of experience-dependent memory traces in hippocampal formation needs further investigation, as signal processing in the hippocampal formation does not always follow a corticofugal route, but is also affected profoundly by thalamofugal signals We should conclude that memory is not a specialized property of a limited set of cortical areas; rather, all areas of the cortex as well as several subcortical structures are xiii xiv Preface capable of experience-dependent change over a wide range of timescales We therefore hope that we will correct the common misconception that the hippocampus is a closed system, self-sufficiently responsible for the declarative memory formation We here would like to thank all the authors of the chapters presented in this volume—there is a considerable body of work to savor here and the pleasant feeling of having one’s pet prejudices tested and changed a little to be enjoyed Shane O’Mara and Marian Tsanov Institute of Neuroscience Trinity College, Dublin CHAPTER If I had a million neurons: Potential tests of corticohippocampal theories Michael E Hasselmo1 Department of Psychological and Brain Sciences, Center for Memory and Brain, Center for Systems Neuroscience, Graduate Program for Neuroscience, Boston University, Boston, MA, USA Corresponding author: Tel.: +617-353-1397; Fax: +617-358-3296, e-mail address: hasselmo@bu.edu Abstract Considerable excitement surrounds new initiatives to develop techniques for simultaneous recording of large populations of neurons in cortical structures This chapter focuses on the potential value of large-scale simultaneous recording for advancing research on current issues in the function of cortical circuits, including the interaction of the hippocampus with cortical and subcortical structures The review describes specific research questions that could be answered using large-scale population recording, including questions about the circuit dynamics underlying coding of dimensions of space and time for episodic memory, the role of GABAergic and cholinergic innervation from the medial septum, the functional role of spatial representations coded by grid cells, boundary cells, head direction cells, and place cells, and the fact that many models require cells coding movement direction Keywords Entorhinal cortex, Stellate cells, Medial septum, Time coding, Spatial coding, Oscillatory interference, Population recording INTRODUCTION The title of this chapter has a number of inspirations The title was partly inspired by a song entitled “If I had a million dollars” by the Canadian band Barenaked Ladies, who humorously sing about the things they would with a million dollars This inspiration explains the title, which is not referring to the author having only a million neurons in his own brain, but to the usefulness of data from a million individual neurons recorded simultaneously in a behaving animal This inspiration also explains the ambitious focus on a million neurons Obviously, research can benefit tremendously from techniques for recording up to a thousand neurons (Dombeck et al., 2010; Gee et al., 2014; Ghosh et al., 2011; Heys et al., 2014; Sheffield and Progress in Brain Research, Volume 219, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.03.009 © 2015 Elsevier B.V All rights reserved CHAPTER A million neurons Dombeck, 2014), and further benefits will also arise from recording tens of thousands of neurons or hundreds of thousands of neurons The scientific inspiration for the title comes as a response to a surprising comment that I have heard from other scientists over the years This comment takes different forms, but the common gist is that recordings of thousands or millions of neurons would not be any more useful than data from current technology I find this comment surprising because it seems obvious how expanding the numbers of neurons would be useful But I have heard the comment multiple times, even from researchers who were instrumental in developing techniques for the current state of the art for multiple single-unit recording in behaving animals So I want to take the opportunity to answer the question in the context of my own area of research This chapter is also inspired by the announcement of the federal BRAIN initiative (Brain Research through Advancing Innovative Neurotechnology) One component of this initiative proposes support for recording of activity in large populations of neurons, showing that many scientists recognize the importance of this type of data But I think the field can benefit from explicit examples of questions that can be answered if we had large populations of neurons in a well-structured data set obtained from an awake, behaving rat with well-described behavior Answering this question not only supports the idea of funding innovative neurotechnology but also provides a framework for presenting some of the interesting current questions in the field As long as I am moving beyond current technology in terms of the number of recorded neurons, I will also assume additional highly desirable features about the data I will assume that the spiking activity of neurons can be observed at a high temporal resolution, such as that obtained with multiple single-unit recording This contrasts with the slower time course of activation data obtained from current techniques for calcium imaging in large populations of neurons I will assume the data are recorded simultaneously over at least 10 in an awake, behaving rat actively moving around its environment I will assume the data include tracking the head direction and movement direction of the behaving rat in space and time I will assume that we can record in multiple different anatomical regions, and, in some cases, that we can identify the individual molecular identity of the neurons in the population I will not initially make any assumptions about knowledge of the connectivity of the neurons, though connectome data would be useful when coupled with data on physiology and molecular identity of neurons and the behavior of the animal CORTICAL CODING OF SPACE If I had data from a million neurons, one top priority would be to analyze how grid cells and place cells are generated Fundamental questions about the nature of spatial representations in the cortex would be answered through an understanding of the mechanisms of generation of the spatial firing patterns of grid cells Extensive data from multiple interacting brain regions should be able to elucidate the mechanism of Cortical coding of space generation of grid cells, and I think it is useful to consider the steps that could be taken with such extensive data The following sections focus on different aspects of this fundamental question, including the possible rate coding of movement direction, the possible phase coding of movement direction and speed, and the coding of sensory cues and boundaries The Nobel prize in physiology or medicine in 2014 acknowledged the importance of grid cells and place cells by recognizing O’Keefe for the discovery of place cells (O’Keefe, 1976; O’Keefe and Dostrovsky, 1971) and May-Britt and Moser for the discovery of grid cells (Fyhn et al., 2004; Hafting et al., 2005; Moser and Moser, 2008) Initially, grid cells were proposed as a mechanism for driving place cells (McNaughton et al., 2006; Solstad et al., 2006), but recent data showing loss of grid cells with inactivation of the hippocampus suggest that place cells might be driving grid cells (Bonnevie et al., 2013) In either case, understanding the generation of one of these phenomena is important to understanding the other The highly regular pattern of grid cell firing gives a sense that they can be accounted for by elegant theoretical principles, and numerous published models address the mechanism of grid cell generation Grid cell models can be grouped into categories based on some of their component features One category of models uses attractor dynamics to generate the characteristic firing pattern of grid cells (Bonnevie et al., 2013; Burak and Fiete, 2009; Bush and Burgess, 2014; Couey et al., 2013; Fuhs and Touretzky, 2006; Guanella et al., 2007; McNaughton et al., 2006) Most of the attractor models use circularly symmetric inhibitory connectivity within a large population of grid cells to generate competition between grid cells coding nearby locations This results in a pattern of neural activity across the population that matches the characteristic hexagonal array of grid cell firing fields (also described as falling on the vertices of tightly packed equilateral triangles) Large-scale recording of cells particularly during first entry to a familiar environment would allow testing of whether the population dynamics appear to settle into an attractor state or whether individual neurons independently code location As noted by the models, the shared orientation and spacing of the firing fields of grid cells within individual modules (Barry et al., 2007; Stensola et al., 2012) and the shared shifts of firing fields with environmental manipulations (Barry et al., 2007; Stensola et al., 2012; Yoon et al., 2013) already support the existence of attractor dynamics However, generating a grid-like pattern across a population is not sufficient for modeling individual grid cells Replicating the changes in firing of an individual grid cell over time requires that the grid-like pattern in the population needs to be shifted in proportion to the behavioral movement of the animal, that is, in proportion to its running velocity To generate this movement, most attractor models of grid cells explicitly cite a role for experimental data on conjunctive grid-by-head-direction cells (Sargolini et al., 2006) In attractor models of grid cells (Burak and Fiete, 2009; Couey et al., 2013; McNaughton et al., 2006), these grid-by-head-direction cells are proposed to drive adjacent neurons within the population based on the movement of the animal However, there is a fundamental problem in using grid-by-headdirection cells to represent the movement direction of an animal, as described in CHAPTER A million neurons Section 2.1 A similar problem occurs for oscillatory interference models of grid cells (Burgess, 2008; Burgess et al., 2007; Hasselmo, 2008) that also require velocity as an input Data show that the movement direction coding required by these models cannot be provided by cells coding head direction 2.1 CODING OF SPACE BASED ON CODING OF MOVEMENT DIRECTION If I had data from a million neurons, I would look for coding of movement direction This would resolve an important paradox about many models of the formation of spatial representations in the cortex This paradox concerns the fact that most models of location coding require movement direction as input, but experimental data show that neurons in these structures primarily code head direction rather than movement direction (Raudies et al., 2014) Many theories of spatial coding by the hippocampus and associated structures propose that the coding of space depends upon path integration (Etienne and Jeffery, 2004; McNaughton and Nadel, 1990; McNaughton et al., 2006; Samsonovich and McNaughton, 1997), which involves the integration of a selfmotion signal of velocity to generate a representation of spatial location These theories are very appealing and have formed the basis for many models of grid cells, including the attractor dynamic models that use a velocity signal to shift the grid cell activity within a population (Burak and Fiete, 2009; Couey et al., 2013; McNaughton et al., 2006) and the oscillatory interference models that use a velocity signal to shift the frequency of velocity-controlled oscillators (Blair et al., 2008; Burgess et al., 2007; Hasselmo, 2008) The problem for these models is the representation of movement direction Path integration specifically requires a representation of a rat’s movement velocity—that is the direction of movement and the speed of movement Most models of this effect cite neurophysiological data in support of this data being available They appropriately cite the neural recordings showing systematic changes in firing rate with running speed (McNaughton et al., 1983; O’Keefe et al., 1998; Wills et al., 2012) However, the problem occurs when justifying the use of movement direction in these modeling studies (Bonnevie et al., 2013; Burak and Fiete, 2009; McNaughton et al., 2006) For movement direction, these papers commonly cite studies showing neurons that respond on the basis of head direction ( Jankowski et al., 2014; Taube, 1995; Taube et al., 1990; Tsanov et al., 2011) Data show robust and welldocumented responses of neurons to head direction in the presubiculum (Taube et al., 1990), anterior thalamus (Taube, 1995; Tsanov et al., 2011), and medial entorhinal cortex (Brandon et al., 2011, 2013; Sargolini et al., 2006) However, there is a fundamental logical flaw to the citation of head direction data for a model requiring movement direction as part of a velocity signal Analysis of behavioral tracking data shows that the behavioral measures of head direction are not equivalent to movement direction in the same rat (Raudies et al., 2014), even when performing a running average over extended periods of different head direction This paradox could be resolved by an exhaustive analysis of the firing properties of neurons in entorhinal cortex, presubiculum, and anterior thalamus relative to References Dragoi, G., Tonegawa, S., 2011 Preplay of future place cell sequences 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strategy switches and reversals in the rat PFC Behav Neurosci 123, 1028–1035 Zagami, M.T., Ferraro, G., Montalbano, M.E., Sardo, P., La Grutta, V., 1995 Lateral habenula and hippocampal units: electrophysiological and iontophoretic study Brain Res Bull 36, 539–543 Index Note: Page numbers followed by f indicate figures A Acetylcholine, 104–105 Angular head velocity, 85–86 Anterior temporal (AT) system affective processing, 53 object perception, 54 recognition and associative memory, 53 semantic processing, 54 Anterograde amnesia, 168 Autoassociation memory architecture, 26f CA3, 23–27 Autobiographical memory, 51 B Backprojections cortical, 30–32, 35f, 37–38 neocortex, 32–34 neocortex to hippocampus, 28–32 pathway, 29 Border cells, 87 Brainstem nuclei, HD cells, 90 C CA3 autoassociation memory, 23–27 stage, 23 CA1 cells, 28, 89 CA1 field, 65–66, 72f Cholinergic neuromodulation hippocampal activity, network level, 106–109 inhibitory septal and hippocampal signaling, 109–110 neuronal spiking in hippocampus, 104–106 Cingulum bundle, 168–169 Cognition contextual fear memory, 152–153 memory persistence, 156 reference memory, 153–155 working memory, 151–152 Context-and reward-prediction error signaling functional pathways, from hippocampus to VTA, 223–227 Contextual fear memory, 152–153 Cortical backprojections, 30–32, 35f, 37–38 Cortical coding of space, 2–9 See also Cortico-hippocampal theories Cortico-hippocampal theories, 2–9 coding of time, 9–10 entorhinal cortex, 7–8 medial septum and entorhinal cortex, 5–7 neurons population, 11–12 rate coding of movement direction, 4–5 replay of episodes, 10–11 sensory features, 8–9 systems, 48f D Decision-making neurocircuitry, 219 See also Predictive memories and adaptive decisions efferent messages from, 222–223 neurobiological language for, 219–222 Default network, 47 Dentate granule cells, 23 Dopamine (DA) and communication through oscillations, 199–201 hippocampal synaptic plasticity, 194 hippocampus-dependent memories, 192–193 hippocampus vs PFC, theta coordination, 200–201 midbrain system, 188–190 mnemonic processing, 202–203 within PFC modulates WM, 197–198 receptors, 190–192 release, 193 role in replay, 194–196 source, 196 and theta rhythm, hippocampus, 200 Dopaminergic tuning hippocampal spatial coding DA modulates hippocampus-dependent memories, 192–193 DA receptors, 190–192 dopaminergic modulation, hippocampal place cells, 193–194 hippocampal synaptic plasticity, 194 mechanisms, 194–196 replay, DA’s role, 194–196 sources, hippocampal DA, 196 terminals, 196 243 244 Index Dopaminergic tuning (Continued) ventral tegmental area (VTA) dopaminergic neurons, 191f midbrain dopaminergic system, 188–190 prefrontal cortex vs hippocampus interactions, spatial WM, 198–201 oscillations, DA and communication, 199–201 plasticity, 199 working memory (WM) modulation, 197–198 signaling salience, 187–188 predictive memories and adaptive decisions, 227–229 spatial coding, dopaminergic tuning DA modulates hippocampus-dependent memories, 192–193 DA receptors, 190–192 dopaminergic modulation, hippocampal place cells, 193–194 hippocampal synaptic plasticity, 194 mechanisms, 194–196 replay, DA’s role, 194–196 sources, hippocampal DA, 196 terminals, 196 ventral tegmental area (VTA) dopaminergic neurons, 191f systems-level functions, 22–23 ventral midline thalamus contextual fear memory, 152–153 electrophysiology, 149–151 memory persistence, 156 reference memory, 153–155 ReRh neurons, 151 ReRh-triggered alterations, 149–150 reuniens nucleus, 147 rhomboid nucleus, 147–148 working memory, 151–152 E Entorhinal cortex (EC), 5–7, 122, 125 cortical coding of space, 7–8 medial septum and, 5–7 Episodic memory, 23, 28–29, 35f PM system, 51 Error signaling in brain, 230–231 Extended hippocampal memory system, 170, 176 See also Subiculum F Fear memory, 136 Fornix, 164, 165f body, 165f crus, 165f postcommissural, 166f, 170–174 Functional connectivity, PMAT framework, 47–48 G Goal representation, 221–222 Grid cells, 87 hippocampal place cell, 87 influence, head direction cells, 94 H Head direction (HD) cells brainstem nuclei, 90 circuit, 85f described, 86–87 DTN and LMN, 91 grid and place cells influence, 94 self-generated movement, 90–91 signal importance, 91–93 Hippocampus, 22–28 circuitry, 23 computation, 23–28 extended (see Subiculum) hippocampo-neocortical recall, memories stored in, 34–38 L Long-term potentiation (LTP), 134 M Mammillary bodies and memory medial diencephalic-temporal lobe interactions, 174–176 Papez’ circuit anatomy, 164–166 function, 166–174 Mammillothalamic tract, 169–170 Medial diencephalic-temporal lobe interactions, 174–176 Medial prefrontal cortex (mPFC) contextual fear memory, 152–153 ReRh activation, 158f working memory, 151–152 Medial septum (MS), 5–7, 122, 126–127 Medial temporal lobe (MTL), 45–46 Memory consolidation, 151 contextual fear, 152–153 episodic, 23, 28–29, 35f generalization, 152–153 pattern association, 35f Index persistence, 156 recall, 28–38 reference, 153–155 spatial, 156 subiculum, 68 working, 151–152 Memory systems, 218–219 efferent messages from, 222–223 neurobiological language for, 219–222 Motor efference copy, 90–91 N Navigation, 83 neural correlation, hippocampal place cell angular head velocity, 85–86 border cells, 87 conjunctive cells, 87 grid cells, 87 HD cells, 86–87 vestibular inputs for, 88–90 Neocortex, 32–34 to hippocampus, 28–32 Neural correlation navigation hippocampal place cell angular head velocity, 85–86 border cells, 87 conjunctive cells, 87 grid cells, 87 HD cells, 86–87 Neuronal spiking in hippocampus, 104–106 Nonhippocampal inputs, mammillary bodies, 174 Nucleus reuniens (RE) anatomy, 128–132 electrophysiology, 133–134 learning and memory, 134–137 O Optogenetics, 106–109 Oscillatory interference, 3–4, 7–9 P Papez’ circuit anatomy, 164–166 dissection, 165f function cingulum bundle, 168–169 mammillothalamic tract, 169–170 nonhippocampal inputs, mammillary bodies, 174 postcommissural fornix, 170–174 Parahippocampal cortex (PHC), 52, 66 Parasubiculum, 66 See also Subiculum Path integration and sensorimotor signals, 110–113 Pattern association, 29 dilution, 34–38 memory, 35f Perirhinal cortex (PRC), 45–46, 53–54 Place cells hippocampal, 87 influence, head direction cells, 94 PMAT framework, 46f anatomical and functional connectivity, 47–48 connectivity vs function, 48–50 foundation, 47 integration sites hippocampus, 55 ventromedial prefrontal cortex, 55–56 in memory and cognition, 50–54 Postcommissural fornix, 166f, 170–174 Posterior medial (PM) system episodic and autobiographical memory, 51 scene perception, 52 social cognition, 52 space and time, 51–52 Postsubiculum, 66, 72–73 See also Subiculum Predictive memories and adaptive decisions, 227–233 accuracy hippocampal evaluation, 227–229 striatal evaluation, 229–230 challenge for future research, 231–233 sensory and motor predictions, 230 Prefrontal cortex (PFC) vs hippocampus interactions, spatial WM, 198–201 oscillations, DA and communication, 199–201 plasticity, 199 working memory modulation, 197–198 Presubiculum, 66, 67 See also Subiculum R Recall memory, 28–38 from hippocampus, 34–38 Reference memory, 153–155 ReRh-triggered alterations direct evidence, 150 indirect evidence, 149–150 Retrosplenial cortex (RSC), 46–47, 51 Reuniens nucleus, 147 Rhomboid nucleus, 147–148, 134–135 See also Nucleus reuniens (RE) S Septo-hippocampal signal processing hippocampal activity, network level, 106–109 245 246 Index Septo-hippocampal signal processing (Continued) inhibitory septal and hippocampal signaling, 109–110 neuronal spiking in hippocampus, 104–106 Spatial memory, 156 Spatial orientation, 84 Stellate cells, Striatal evaluation, 229–230 Subiculum connectivity, 68–72 extrinsic afferents, 70–71 extrinsic projections, 69–70 topographic organization, 71–72 function, 76–78 lesion studies, 72–76 electrophysiological findings, 75–76 functional mapping, immediate-early genes, 75 nomenclature, 66–67 structure, 66–67, 76–78 Supramammillary nucleus (SUM) anatomy, 122–124 electrophysiology, 124–125 learning and memory, 127–128 theta rhythm, 125–127 T Tetrodotoxin (TTX), 89 Theta rhythm, 150 coherence coordination, hippocampus vs PFC, 200–201 rhythm, dopamine and, 200 oscillations, inhibitory septal and hippocampal signaling, 109–110 SUM, 125–127 Trajectory-dependent neurons, 136–137 V Ventral midline thalamus, hippocampal functions cognition contextual fear memory, 152–153 memory persistence, 156 reference memory, 153–155 working memory, 151–152 connectivity reuniens nucleus, 147 rhomboid nucleus, 147–148 electrophysiology, 149–151 ReRh neurons, 151 ReRh-triggered alterations direct evidence, 150 indirect evidence, 149–150 Ventral tegmental area (VTA) decision systems, 219 dopamine neural responses regulation, 224f dopaminergic neurons, 191f W Working memory (WM), 151–152 modulation, 197–198 SUM, learning and memory, 127 Other volumes in PROGRESS IN BRAIN RESEARCH Volume 167: Stress 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Enhancing Performance for Action and perception: Multisensory Integration, Neuroplasticity and Neuroprosthetics: Part I, by Andrea M Green, C Elaine Chapman, John F Kalaska and Franco Lepore (Eds.) – 2011, 978-0-444-53752-2 Volume 192: Enhancing Performance for Action and Perception: Multisensory Integration, Neuroplasticity and Neuroprosthetics: Part II, by Andrea M Green, C Elaine Chapman, John F Kalaska and Franco Lepore (Eds.) – 2011, 978-0-444-53355-5 Volume 193: Slow Brain Oscillations of Sleep, Resting State and Vigilance, by Eus J.W Van Someren, Ysbrand D Van Der Werf, Pieter R Roelfsema, Huibert D Mansvelder and Fernando H Lopes da Silva (Eds.) – 2011, 978-0-444-53839-0 Volume 194: Brain Machine Interfaces: Implications For Science, Clinical Practice And Society, by Jens Schouenborg, Martin Garwicz and Nils Danielsen (Eds.) – 2011, 978-0-444-53815-4 Volume 195: Evolution of the Primate Brain: From Neuron to Behavior, by Michel A Hofman and Dean Falk (Eds.) – 2012, 978-0-444-53860-4 Volume 196: Optogenetics: Tools for Controlling and Monitoring Neuronal Activity, by Thomas Kn€opfel and Edward S Boyden (Eds.) – 2012, 978-0-444-59426-6 Volume 197: Down Syndrome: From Understanding the Neurobiology to Therapy, by Mara Dierssen and Rafael De La Torre (Eds.) – 2012, 978-0-444-54299-1 Volume 198: Orexin/Hypocretin System, by Anantha Shekhar (Ed.) – 2012, 978-0-444-59489-1 Volume 199: The Neurobiology of Circadian Timing, by Andries Kalsbeek, Martha Merrow, Till Roenneberg and Russell G Foster (Eds.) – 2012, 978-0-444-59427-3 Volume 200: Functional Neural Transplantation III: Primary and stem cell therapies for brain repair, Part I, by Stephen B Dunnett and Anders Bj€orklund (Eds.) – 2012, 978-0-444-59575-1 Volume 201: Functional Neural Transplantation III: Primary and stem cell therapies for brain repair, Part II, by Stephen B Dunnett and Anders Bj€orklund (Eds.) – 2012, 978-0-444-59544-7 Volume 202: Decision Making: Neural and Behavioural Approaches, by V.S Chandrasekhar Pammi and Narayanan Srinivasan (Eds.) – 2013, 978-0-444-62604-2 Volume 203: The Fine Arts, Neurology, and Neuroscience: Neuro-Historical Dimensions, by Stanley Finger, Dahlia W Zaidel, Franc¸ois Boller and Julien Bogousslavsky (Eds.) – 2013, 978-0-444-62730-8 Volume 204: The Fine Arts, Neurology, and Neuroscience: New Discoveries and Changing Landscapes, by Stanley Finger, Dahlia W Zaidel, Franc¸ois Boller and Julien Bogousslavsky (Eds.) – 2013, 978-0-444-63287-6 Volume 205: Literature, Neurology, and Neuroscience: Historical and Literary Connections, by Anne Stiles, Stanley Finger and Franc¸ois Boller (Eds.) – 2013, 978-0-444-63273-9 Volume 206: Literature, Neurology, and Neuroscience: Neurological and Psychiatric Disorders, by Stanley Finger, Franc¸ois Boller and Anne Stiles (Eds.) – 2013, 978-0-444-63364-4 Volume 207: Changing Brains: Applying Brain Plasticity to Advance and Recover Human Ability, by Michael M Merzenich, Mor Nahum and Thomas M Van Vleet (Eds.) – 2013, 978-0-444-63327-9 Volume 208: Odor Memory and Perception, by Edi Barkai and Donald A Wilson (Eds.) – 2014, 978-0-444-63350-7 Volume 209: The Central Nervous System Control of Respiration, by Gert Holstege, Caroline M Beers and Hari H Subramanian (Eds.) – 2014, 978-0-444-63274-6 Volume 210: Cerebellar Learning, Narender Ramnani (Ed.) – 2014, 978-0-444-63356-9 Volume 211: Dopamine, by Marco Diana, Gaetano Di Chiara and Pierfranco Spano (Eds.) – 2014, 978-0-444-63425-2 Volume 212: Breathing, Emotion and Evolution, by Gert Holstege, Caroline M Beers and Hari H Subramanian (Eds.) – 2014, 978-0-444-63488-7 Volume 213: Genetics of Epilepsy, by Ortrud K Steinlein (Ed.) – 2014, 978-0-444-63326-2 Volume 214: Brain Extracellular Matrix in Health and Disease, by Asla Pitkaănen, Alexander Dityatev and Bernhard Wehrle-Haller (Eds.) – 2014, 978-0-444-63486-3 Other volumes in PROGRESS IN BRAIN RESEARCH Volume 215: The History of the Gamma Knife, by Jeremy C Ganz (Ed.) – 2014, 978-0-444-63520-4 Volume 216: Music, Neurology, and Neuroscience: Historical Connections and Perspectives, by Francáois Boller, Eckart Altenmuăller, and Stanley Finger (Eds.) – 2015, 978-0-444-63399-6 Volume 217: Music, Neurology, and Neuroscience: Evolution, the Musical Brain, Medical Conditions, and Therapies, by Eckart Altenmuăller, Stanley Finger, and Francáois Boller (Eds.) 2015, 978-0-444-63551-8 Volume 218: Sensorimotor Rehabilitation: At the Crossroads of Basic and Clinical Sciences, by Numa Dancause, Sylvie Nadeau, and Serge Rossignol (Eds.) – 2015, 978-0-444-63565-5 249 ... also inspired by the announcement of the federal BRAIN initiative (Brain Research through Advancing Innovative Neurotechnology) One component of this initiative proposes support for recording... experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety... properties of grid cell firing fields recorded in behaving animals (Hafting et al., 2005; Sargolini et al., 2006) and the intrinsic properties of medial entorhinal neurons recorded intracellularly (Boehlen