Inhibitory control of hippocampal memory formation
Memories are stored by ensembles of neurons active at the time of encoding. How neurons composing these ensembles are selected and eventually transformed in the cellular substrate of memory is however, unknown. Here, we study the role of inhibitory microcircuits in the allocation of memory in the Dentate Gyrus (DG) of the hippocampus. We reveal that granule cells (GCs) of the DG are in competition between each other during formation of a contextual fear memory. Active neurons of the memory ensemble trigger the activity of somatostatin INs that in turn prevent neighbouring GCs to become part of it and determine memory stability.
Moreover, we show that GABAergic synapses in GCs and SST+ INs determine the size of the active ensemble and memory stability. Altogether we reveal two different components of the inhibitory system of the DG that control allocation of memory in hippocampal circuits: INs and GABAergic synapses.
STEFANELLI, Thomas. Inhibitory control of hippocampal memory formation. Thèse de doctorat : Univ. Genève et Lausanne, 2017, no. Neur. 209
DOI : 10.13097/archive-ouverte/unige:97729 URN : urn:nbn:ch:unige-977298
Disclaimer: layout of this document may differ from the published version.
FACULTÉ DE PSYCHOLOGIE ET DES SCIENCES DE L’ÉDUCATION
DOCTORAT EN NEUROSCIENCES des Universités de Genève
et de Lausanne
UNIVERSITÉ DE GENÈVE FACULTÉ DE PSYCHOLOGIE
Professeur Dr. méd. Christian Lüscher, directeur de thèse Dr. Pablo Mendez, co-directeur de thèse
TITRE DE LA THÈSE
INHIBITORY CONTROL OF HIPPOCAMPAL MEMORY FORMATION
THESE Présentée à la Faculté de Médecine de l’Université de Genève
pour obtenir le grade de Docteur en Neurosciences
de Lugano (Tessin)
Thèse N° 209 Genève
Editeur ou imprimeur : Université de Genève 2017
My greatest thank goes to Professor Dominique Muller, for giving me the chance to start my PhD thesis in his laboratory. Dominique was more than a scientist to me, he was a rare and humble mentor but more importantly, he was an exceptional human being and today is one of the most shining stars in the universe.
I would like to express my sincerest gratitude to a very special person: my supervisor and extraordinary mentor Dr. Pablo Mendez, who thought me with extreme patience how to become a humble scientist and whose support, knowledge, time and passion for research motivated me in these years. Every student should have the chance to have a person like Pablo in their PhD life.
A great appreciation and gratitude go to Professor Christian Lüscher, a remarkable mentor, outstanding person and brilliant scientist, who supported me with immense energy and motivation to complete my PhD thesis after Dominique Muller passed away.
I would like to thank all the previous members of the Muller laboratory for their collegiality, support and patience with me: Dr. Yann Bernardinelli, Dr. Cristina Bertollini, Dr.
Mathias DeRoo, Dr. Carmen Flores, Dr. Esther Giraldo, Marie-Priscille Hervé, Lorena Jourdain, Dr. Thomas Marissal, Dr. Enora Moutin and Dr. Iryna Nikonenko.
I thank all the members of the Lüscher laboratory: Dr. Sebastiano Bariselli, Julie Corre, Dr. Meaghan Creed, Jennifer Hauet, Agnès Hiver, Dr. Yves Kramer, Dr. Sandrine Lefort, Dr. Michaël Loureiro, Dr. Niels Ntamati, Dr. Eoin O’Connor, Dr. Vincent Pascoli, Catherine Pham, Dr. Linda Simmler, Prof. Kelly Tan, Dr. Jean Terrier and Sarah Thoeni.
A thank also goes to all the members of the department of basic neuroscience of Geneva, for their support and help at any time.
An enormous thank goes to my family, especially to my beloved parents and brother for always believing in me, for supporting me with infinite patience, without whom I could not have made any of this.
A very special thank also to Carlo Turlon, Cristina Fabbri and their families for their time, support and for always being next to me.
Thanks to my girlfriend, closest/best friends (no need to list them as they know who they are), friends, acquaintances and all the people that interacted with me during my life, for their presence, for believing in me and for their continuous support.
I dedicate this work to all of you.
Recent evidence in the field of memory research suggests that the cellular substrate of memory is formed by ensembles of neurons active during encoding that undergo experience-dependent cellular and synaptic plasticity and eventually become necessary and sufficient for memory recall (Tonegawa et al., 2015; Josselyn et al., 2015;
Eichenbaum, 2016). Little is known about how such ensembles of neurons emerge during acquisition and eventually form the cellular engram. Previous studies have shown that memory is allocated to neuronal ensembles according to the levels of activity of individual neurons (Yiu et al., 2014). Neurons that are more active are more likely to become part of the cellular engram while those that are less active will be excluded. However, neuronal networks are composed of highly interconnected neurons. Therefore, it is possible that interaction between different neurons of the network may be determinant in their selection.
In addition, the activity of engram neurons is tightly controlled by a huge diversity of interneurons (INs) (Markram et al., 2004; Klausberger and Somogyi, 2008). Accordingly, a critical question remains unanswered: Do INs play a role in the formation of the cellular engram?
The present work studies the role of inhibitory microcircuits in the selection of engrams during hippocampal-dependent memory formation. We focus our attention on the dentate gyrus (DG) as it has been shown that a small fraction of granules cells (GCs) form context specific ensembles of neurons that are sufficient and necessary for mnemonic representations (Liu et al., 2012; Denny et al., 2014; Ryan et al., 2015). Accordingly, we propose to approach the question from two different perspectives that conferred two parts to this project: 1) Investigate the role of INs diversity in the formation and function of contextual fear memory cellular engrams; 2) Study the role of inhibitory synapses formation in the encoding of a contextual fear memory.
In the first part of this work we focus on the initial phase of memory, the encoding of a contextual fear memory in mice and the formation of the cellular engram. By manipulating the activity of GCs of the DG, we revealed a mechanism of lateral inhibition that modulates the size of the cellular engram. Active GCs engage somatostatin-positive (SST+) INs that inhibit the dendrites of surrounding GCs, excluding them from the engram. Our findings reveal a microcircuit within the DG that controls the size of the cellular engram and the stability of contextual fear memory.
In the second part of this work we study how inhibitory GABAergic synapses control the
activity of principal excitatory neurons. By manipulating phosphorylation of Gephyrin, a master regulator of GABAergic synapses, we altered the function and structure of GABAergic synapses and investigated their role in hippocampal-dependent memory formation. With the help of viral constructs expressing Gephyrin mutants in specific neuronal populations, we revealed that blockade of Gephyrin S305 phosphorylation reduces GABAergic synapses of excitatory cells and SST+ INs in the dorsal hippocampus.
These manipulations alter the size of the cellular engram and affect memory stability. The results suggest that GABAergic synapses in distinct neuronal types regulate hippocampal dependent cellular engram formation.
To conclude, in the first part, our results suggest that GCs are in competition between each other during memory formation. Active cells of the cellular engram trigger SST+ INs activity that in turn prevent neighboring GCs to become part of it. Second, we showed that GABAergic synapses of GCs and SST+ INs affect the size of the cellular engram and memory stability. This second mechanism, which seems to be mainly post-synaptic, suggests that GABAergic synapses can determine the formation of the cellular engram and affect memory function.
Altogether, we revealed a common phenomenon: the GABAergic mechanisms that determine the size of the cellular engram and memory stability that we assessed by manipulating two different components of the inhibitory system of the DG: INs and GABAergic synapses.
Des preuves récentes dans le domaine de la recherche sur la mémoire suggèrent que le substrat cellulaire de la mémoire est formé par des ensembles de neurones actifs pendant l'encodage qui subissent une plasticité cellulaire et synaptique dépendante de l'expérience et qui s’avèrent nécessaires et suffisants pour le rappel de la mémoire (Tonegawa et al., 2015 ; Josselyn et al., 2015 ; Eichenbaum, 2016). Notre connaissance sur la façon dont ces ensembles de neurones apparaissent lors de l'acquisition et finissent par former l'engramme cellulaire est encore très limitée. Des études antérieures ont montré que la mémoire est attribuée aux ensembles neuronaux en fonction des niveaux d'activité des neurones individuels (Yiu et al., 2014). Les neurones les plus actifs sont plus susceptibles de faire partie de l'engramme cellulaire alors que ceux qui sont moins actifs seront exclus.
Cependant, les réseaux neuronaux sont composés de neurones hautement interconnectés. Par conséquent, il est possible que l'interaction entre différents neurones du réseau puisse être déterminante dans leur sélection. De plus, l'activité des neurones de l'engramme est étroitement contrôlée par une grande diversité d'interneurones (INs) (Markram et al., 2004 ; Klausberger et Somogyi, 2008). Par conséquent, une question critique reste sans réponse: Les INs jouent-ils un rôle dans la formation de l’engramme cellulaire?
Le présent travail étudie le rôle des microcircuits inhibiteurs dans la sélection des engrammes lors de la formation de la mémoire dépendante de l'hippocampe. Nous concentrons notre attention sur le gyrus denté (DG), car il a été démontré qu'une petite fraction de cellules granulaires (GCs) forment des ensembles spécifiques de neurones qui sont suffisants et nécessaires pour les représentations mnémoniques (Liu et al., 2012;
Denny et al., 2014; Ryan et al., 2015). Par conséquent, nous proposons d'aborder la question à partir de deux points de vue différents qui confèrent deux parties à ce projet: 1) Étudier le rôle de la diversité des INs dans la formation et la fonction des engrammes cellulaires de la mémoire contextuelle; 2) Etudier le rôle de la formation des synapses inhibitrices dans le codage d'une mémoire contextuelle de la peur.
Dans la première partie de ce travail, nous nous concentrons sur la phase initiale de la mémoire : l'encodage d'une mémoire contextuelle de la peur chez la souris et la formation de l'engramme cellulaire. En manipulant l'activité des GCs du DG, nous avons révélé un mécanisme d'inhibition latérale qui module la taille de l'engramme cellulaire. Les GCs activés engagent des INs positives pour la somatostatine (SST+) qui inhibent les dendrites
des GCs environnantes, en les excluant de l’engramme cellulaire. Nos résultats révèlent un microcircuit au sein du DG qui contrôle la taille de l'engramme cellulaire et la stabilité de la mémoire contextuelle de la peur.
Dans la deuxième partie de ce travail, nous étudions comment les synapses inhibitrices GABAergiques contrôlent l'activité des neurones excitateurs principaux. En manipulant la phosphorylation de la Gephyrin, une protéine régulatrice principale des synapses GABAergiques, nous avons altéré la fonction et la structure des synapses GABAergiques et avons étudié leur rôle dans la formation de la mémoire dépendante de l'hippocampe.
Avec l'aide de constructions virales exprimant des mutants de Gephyrin dans des populations neuronales spécifiques, nous avons révélé que le blocage de la phosphorylation de Gephyrin à la Serine 305 réduit les synapses GABAergiques des cellules excitatrices et les INs à la SST+ dans l'hippocampe dorsal. Ces manipulations altèrent la taille de l'engramme cellulaire et affectent la stabilité de la mémoire. Les résultats suggèrent que les synapses GABAergiques dans différents types de neurones régulent la formation de l’engramme cellulaire dépendant de l'hippocampe.
Pour conclure, dans la première partie, nos résultats suggèrent que les GCs sont en concurrence entre elles pendant la formation de la mémoire. Les cellules actives de l’engramme cellulaire déclenchent l'activité des INs à la SST+ qui, à leur tour, empêchent les GCs voisines de faire partie de celui-ci. Deuxièmement, nous avons montré que les synapses GABAergiques des GCs et des INs à la SST+ affectent la taille de l'engramme cellulaire et la stabilité de la mémoire. Ce deuxième mécanisme, qui semble être principalement post-synaptique, suggère que les synapses GABAergique peuvent déterminer la formation de l'engramme cellulaire et affecter le fonctionnement de la mémoire.
Pour conclure, nous avons révélé un phénomène commun : des mécanismes GABAergiques qui déterminent la taille de l'engramme cellulaire et la stabilité de la mémoire que nous avons évaluée en manipulant deux composants différents du système inhibiteur du DG: les INs et les synapses GABAergiques.
LIST OF ABBREVIATIONS
AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid AP5 Amino-5-phosphonovaleric acid
BC Basket cell
CA Cornu ammonis
CAMKII Calcium/calmodulin-dependent kinase II cAMP Cyclic adenosine monophosphate
ChR2 Channelrhodopsin 2
CNQX 6-cyano-7-nitroquinoxaline-2,3-dione CNS Central nervous system
CR Conditioned response
CREB cAMP response element binding protein CS Conditioned stimulus
DG Dentate gyrus
DREADD Designer receptors exclusively activated by designer drugs
EC Entorhinal cortex
GABA ɣ -aminobutyric acid
GCs Granule cells
HF Hippocampal formation
HIPP Hilar perforant path-associated
IEG Immediate early gene
LEC Lateral entorhinal cortex LTD Long-term depression
LTM Long-term memory
LTP Long-term potentiation MEC Medial entorhinal cortex MeCP2 Methyl-CpG binding protein MTL Medial temporal lobe
MWM Morris water maze
NAc Nucleus accumbens
NYP Neuropeptide Y
O-LM Oriens lacunosum-moleculare PER Perirhinal cortex
PKA Protein kinase A
PMDAT Plus-maze discriminative task PNS Peripheral nervous system POR Postrhinal cortex
PR Parahippocampal region
RSC Retrosplenial cortex
RTF Regulatory transcription factor SNc Substantia nigra pars compacta SRF Serum response factor
tTA Tetracycline transactivator
UR Unconditioned response US Unconditioned stimulus
VDCC Voltage-dependent calcium channel VIP Vasoactive intestinal polypeptide VTA Ventral tegmental area
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ... 2
ABSTRACT ... 3
RESUME ... 5
LIST OF ABBREVIATIONS ... 7
TABLE OF CONTENTS ... 9
LIST OF FIGURES ... 12
INTRODUCTION ... 14
1. Story of human memory... 14
1.1 What is memory? ... 14
1.1.1 Memory: a very old concept ... 14
1.2 Different forms of memories and their classification ... 15
1.3 Associational learning as a model to study memory ... 17
1.4 Finding the locus of memory in the brain ... 18
1.4.1 19th century, the advent of experimental research ... 18
1.4.2 First evidence of the physical location of memory in the brain ... 18
2. The anatomy of the hippocampus ... 20
2.1 The medial temporal lobe (MTL) is composed of the hippocampus (HPC) and Brodmann areas 35-36 ... 20
2.2 Hippocampal connectivity: within and between brain regions ... 23
3. Functional roles of hippocampus ... 26
3.1 Hippocampal function ... 26
3.2 The role of the hippocampus in memory ... 27
3.2.1 Pattern separation and completion as mechanisms to store and retrieve memories ... 28
3.2.2 Trisynaptic pathway vs direct pathway ... 29
3.3 Hippocampal neurogenesis and memory ... 29
4. Synaptic plasticity and memory function ... 30
4.1 LTP underlies memory function ... 30
4.2 Synaptic plasticity modifies gene expression ... 32
4.3 Structural plasticity during leaning ... 34
4.4 System consolidation: a model for the long-term storage of memories ... 35
5. GABAergic transmission and memory ... 36
5.1 Inhibitory plasticity is regulated by neuronal activity ... 36
5.2 Hippocampal cells diversity and the role of INs cells types ... 38
5.3 Role of INs diversity in spatial information processing and memory ... 40
6. Towards modern theories of memory ... 42
6.1 Cell assemblies and their formation ... 42
6.2 An old yet correct hypothesis: engrams ... 43
6.3 Experimental evidence of the cellular engram theory ... 45
6.3 Memory allocation and neuronal circuits ... 52
7. Aims of the project ... 55
MATERIALS AND METHODS ... 58
RESULTS ... 64
1. Hippocampal somatostatin interneurons control the size of the neuronal memory ensemble ... 64
1.1 Results ... 65
1.1.1 Neuronal activity during spatial exploration determines the active ensemble 65 1.1.2 Manipulation of the active neuronal ensemble affects contextual fear memory ... 67
1.1.3 Interneurons are activated during SE ... 70
1.1.4 SST+ INs provide inhibition to GCs dendrites ... 72
1.1.5 SST+ INs regulate memory strength and distribution ... 73
1.2 Supplementary Figures ... 76
2. GABAegic synapses regulate hippocampal dependent memory formation ... 82
2.1 Results ... 83
2.1.1 Gephyrin S305 phosphorylation affects GABAergic synapse structure and function of granule cells in the dorsal hippocampus. ... 83
2.1.2 Gephyrin S305 phosphorylation regulates GABAergic synapse structure of somatostatin interneurons in the dorsal hippocampus. ... 85
2.1.3 Gephyrin S305 phosphorylation in GCs alters the size of the active ensemble in the dorsal hippocampus. ... 86
2.1.4 Gephyrin S305 phosphorylation controls fear memory stability and engram size in GCs and SST+ INs. ... 86
DISCUSSION ... 90
Part 1. A cellular mechanism of memory allocation ... 90
Part 2. A synaptic mechanism of memory allocation ... 97
3. General discussion and perspectives ... 100
REFERENCES ... 105
ARTICLES ... 125
LIST OF FIGURES
Figure 1. Human Memory. ... 16
Figure 2. Memory stages. ... 17
Figure 3. Anatomy of the rodent’s hippocampus, adapted from Moser et al. (2014) and Van Strien et al., (2009). ... 21
Figure 4. Basic circuits of the hippocampus, adapted from Neves et al. (2008). ... 23
Figure 5. NMDAr-dependent long-term potentiation (LTP) and depression (LTD) in the hippocampus, from Lüscher and Malenka (2012). ... 30
Figure 6. Network of activity-dependent induction of gene expression, adapted from Flavell and Greenberg (2008). ... 33
Figure 7. Structural plasticity reflects learning, adapted from Caroni et al. (2012). ... 35
Figure 8. Structural plasticity of GABAergic synapses, adapted from Flores and Mendez (2014). ... 37
Figure 9. Basic local circuits in the hippocampus. ... 38
Figure 10. Rodent’s dentate gyrus: morphological classification of interneurons, from Per Anderson et al. (2007). ... 40
Figure 11. Hippocampal connectivity supporting oscillatory activity, from Klausberger and Somogyi 2008. ... 40
Figure 12. Memory expression: sufficiency vs necessity. ... 44
Figure 13. Fear conditioning paradigm in rodents, adapted from Maren et al. 2013. ... 45
Figure 14. Different strategies for the study of memory engrams. ... 45
Figure 15. Optogenetic stimulation of a hippocampal engram activates fear memory recall, adapted from Liu et al. 2012. ... 49
Figure 16. Creating a false memory in the hippocampus, adapted from Ramirez et al. (2013). ... 50
Figure 17. Silencing hippocampal fear engrams interfere with memory recall, adapted from Denny et al. (2013). ... 51
Figure 18. CREB overexpression in amygdala neurons increases fear memory expression, adapted from Han et al. (2007, 2009) and Yiu et al. (2014). ... 54
Figure 19. Neuronal activity regulates the size of active neuronal ensembles ... 66
Figure 20. Optogenetic activation of a fraction of GCs during training disrupts natural recall and creates an artificial memory trace ... 68
Figure 21. Inactivation of a fraction of GCs during training improves memory and increases the size of the recall population by preferentially recruiting non-inactivated neurons ... 70
Figure 22. Dendrite targeting interneurons and GCs are preferentially activated during spatial exploration ... 71 Figure 23. Sparse population of DG excitatory cells activate strong dendritic targeting lateral inhibition ... 73 Figure 24. Dendritic but not somatic targeting INs control the size of the active neuronal population and memory formation ... 75 Figure S1. Optogenetic and chemogenetic control of GC activity. ... 76 Figure S2. Optogenetic modulation of excitatory neuron in the DG does not affect exploration, shock reactivity or memory of an unrelated context. Measurement of freezing behavior from averaged epochs of on/off light. ... 77 Figure S3. CNO treatment specifically affect memory formation only in mice expressing the DREADD receptors. ... 78 Figure S4. Construction of the virally encoded reporter of synaptic activity based on the promoter region of the Arg3.1/Arc gene (AAV-SARE-GFP). ... 79 Figure S5. Chemogenetic control of inhibitory neuron activity. ... 80 Figure S6. Optogenetic and chemogenetic control of PV+ INs activity does not modify engram size and function. ... 81 Figure 25. CamKII mediated Gephyrin phosphorylation affects GABAergic synapse function and structure in hippocampal GCs. ... 84 Figure 26. CamKII mediated Gephyrin phosphorylation affects GABAergic synapse structure in hippocampal SST+ INs. ... 85 Figure 27. GABAergic synapse plasticity alters the size of the active ensemble. ... 86 Figure 28. GABAergic synapse plasticity controls fear memory stability and engram size.
1. Story of human memory
1.1 What is memory?
Memory is defined as the brain process of encoding, storing and retrieving information from the external and internal world. Memory is essential for animal survival and gives human beings the extraordinary ability to develop consciousness and ultimately the self.
Memory processes have been continuously refined and adapted throughout evolution of all the different species to eventually give rise to the today’s civilization. A quote of Elie Wiesel says:
“Without memory there is no culture. Without memory there would be no civilization, no society, no future”
If we ponder a moment on this sentence, we realize how important it is to study the cellular and circuit basis of memory to better understand human functioning.
1.1.1 Memory: a very old concept
The understanding of what memory is and how it works has a long history that can be traced back to ancient Greek philosophers (more than 2000 years ago). We can find written traces on the question of memory with Plato (in the Theaetetus, 191c, d) as well as in his pupil Aristotle’s treatise De memoria et reminiscentia (Smith and Ross, 1908). Plato believed that “memory serves as a bridge between the perceptual world and a rational world of idealized abstractions” (Radvansky, 2006) while Aristotle’s idea was that
“memories are mainly composed of associations among various stimuli or experiences”.
Another vision of Aristotle on memory, that came from Plato, can be found in his major treatise On the Soul in which he postulates that every human is born free of any knowledge and that memory consists of the accumulation of their life experiences; in other words when we born our brain is a “tabula rasa” or “blank state”. Even though their ideas about memory were not completely far from modern concepts of memory, there was no experimental or physical evidence of their statements. For this reason, their assertions remained undemonstrated for years. It is only around the 1800s, when a German philosopher and psychologist, Hermann Ebbinghaus (1850-1909), proposed that complex mental processes like memory could be studied by experimental research. He was one of
the precursors to first develop a scientific approach to study memory, experimental psychology. Ebbinghaus also participated to the establishment of the associationism movement (derived from empiricism: knowledge derives primarily from sensory experiences), which assumes that complex mental processes originate from more simple processes that associate between themselves. His major contribution to experimental study on memory are the discovery of the forgetting curve (the decline of retaining information through time), the spacing effect (learning is more performing if spaced in time) and it is considered to be one of the first that described the learning curve (learning increases in function of experience).
Despite the limitations of his work (i.e. he was the only subject for his studies) Ebbinghaus is considered today as fundamental and precursor of experimental research on memory.
1.2 Different forms of memories and their classification
In the early 1980s, it became generally accepted that memory exists under different forms.
This change became true thanks to human research, in parallel to animal experimentation, using a collection of learning tasks. Memory is a general term that covers a variety of multiple forms. Usually it encompasses the acquisition, retention and recall of information and knowledge, the use of skills, habits, experiences,…. but not only, memories are also depicted by their duration (short or long term period), and by the fact whether they are conscious (explicit) or unconscious (implicit).
A common way to investigate such puzzling organization is to assume that memory, in all its components and forms, is supported by multiple memory systems: well organized cognitive components, distributed across multiple neuronal networks (brain areas) that cooperate to perform distinct functions (Frankland et al., 2006; Kandel et al., 2014). In other words, distinct brain regions process and store different kinds of information. It is thus of major relevance to identify and study these memory systems in order to gain insight into memory functioning.
One of the first researchers to propose a distinction in memory was Wiliam James (1842- 1910) who believed that memory deferred from habits (1980). From a psychology point of view, habits are defined as automatic-everyday behaviors that are continuously repeated without focusing our attention to their accomplishment. Habits are learned without intention and are mostly unconscious. Almost one decade later, McDoughall firstly proposed a differentiation between declarative (explicit) and procedural (implicit) memory, subsequently confirmed by experimental research (Figure 1). The former is the memory of people, things, places, events and facts that can be consciously recalled (or as the term
says: declared). The latter, procedural memory, is based on an action-motor system. It consists of memories of skills and more generally on how to do things that are acquired and recalled unconsciously.
The experimental psychologist Endel Tulving (1927-) further divided declarative memory into episodic memory and semantic memory. Episodic memory is described as the autobiographical memory of personal events of our life that can be consciously (explicitly) recollected (e.g. recalling the day of your thesis defense). On the other side, semantic memory is the memory of facts or events that can explicitly be stored and recalled, in other words, the general knowledge that we acquire throughout life (e.g. recall the fact that Switzerland is a federal republic that consist of 26 Cantons and the city of Bern is the seat of the federal authorities).
Figure 1. Human Memory.
Different processes compose human memory. Memory can be categorized into explicit (conscious) and implicit (unconscious) memories. Implicit memory consists of procedural memory while explicit memory englobes declarative memory that can be further separated in episodic and semantic memories.
Memory can be classified into three different types: sensory memory, short-term memory (also known as working memory) and long-term memory (LTM). Sensory memory is the capacity to retain impressions of information coming from the sensory system after the original stimuli have ceased. It is indeed the shortest element of memory, and it is commonly defined as a buffer system for sensory information (typically few seconds).
Short-term memory is the ability to retain for a very short period of time (typically from 10- 15 seconds to up to 30-60 seconds) a limited amount of information (generally around 5 to 7 items) that can be used immediately but cannot be manipulated. Finally, we have long- term memory, which is the capacity to store information over long periods of time (from minutes to years).
Human memory has four different stages: encoding, consolidation, storage, and recall (Figure 2). Encoding is the ability to transduce the perceived stimuli
Human Memory Implicit Memory Explicit
(item/context/event/person) of interest into a concept (or product) that can be stored in the brain (i.e. pattern of neuronal activity bearing the stimuli’ information). Consolidation is the process of stabilizing the encoded information. Storage is the process by which the brain can arrange the information that could be used later (recall). Recall or retrieval is the ability to take the information out of the repository and use it.
Figure 2. Memory stages.
Memory starts with the encoding of information from the external world. External information is firstly encoded: perceived stimuli are transduced into pattern of neuronal activity. The information is then consolidated. The brain can store the consolidated information by reorganizing it. The information can be recalled to be used again.
1.3 Associational learning as a model to study memory How researchers study memory?
The brain learns to form associations of daily life events that can be recalled. These associations are the building blocks to form brain representations of the external and internal world and have the potential to shape our behavior. From a psychology point of view, learning is the process that accommodates the formation of associations between the environment and behavior. To study memory, researchers use associative learning induced through different forms of conditioning. One of the most commonly used is classical conditioning in which a previously neutral stimulus is paired with another that produces a strong biological reaction.
Classical conditioning was firstly observed by the Russian physiologist Ivan Pavlov (1849-1936), also known as Pavlovian conditioning. In his experiment, Pavlov placed previously food-deprived dogs on a stand where they were restrained and gave them food (defined as the unconditioned stimulus, US). At the only presentation of food dogs started salivating (defined as the unconditioned response, UR). Pavlov then paired a tone (defined as the conditioned stimulus, CS) with the presentation of the food, creating a CS-US relationship. After repeated pairing of CS-US, dogs started salivating at the presentation of the tone alone (CS), creating what is called a conditioned response (CR). Pavlovian conditioning is known to be one of the most powerful learning paradigms to induce adaptive behavior that resist in time.
Watson and Rayner in 1920 tested for the first time this form of conditioning in humans
ENCODING CONSOLIDATION STORAGE RECALL
with the famous study of “little Albert”, a 9-months old child (Watson and Rayner, 2000).
Albert learned to associate (fear) the presence of a white rat (CS) with a very disturbing loud noise (US) that made him cry of fear (UR). Albert was previously presented with a variety of stimuli such as rats, rabbit, monkeys and mask. The fear conditioning of Albert lasted for around 7 weeks with multiple trials per day. Finally, Albert started crying at the presentation of the CS alone (rat). With time, Albert started a process known as fear generalization, he basically feared also all the different animals that shared similar characteristics with the rat (white color, hairs, size,…). The idea behind Watson and Rayner experiment was to induce phobia (a fear that is out of proportion to the danger) by using classical conditioning. Their results provide evidence that conditional learning seems to be efficient in humans and that emotional conditioned responses persist in time.
1.4 Finding the locus of memory in the brain
This led to the idea that changes within the brain may occur and that learned information could be stored somewhere in the brain.
1.4.1 19th century, the advent of experimental research
With the advent of experimental research in the 19th century, questions on memory begun to find some answers. Pioneer neuropsychologist Karl Lashley (1890-1958), inspired by Richard Semon’s book “The Mneme” in 1921, believed that memory was physically located in a specific brain area (see chapter 6.2). To test this idea, he performed lesions in the brain of rats and monkeys and tested their memory performance after surgical interventions. Because of his limited methodology and the restricted knowledge on memory itself (i.e. he did not differentiate short-term from long-term memory and do not considered the possibility that memories, according to their nature could be stored in different areas) he was unable to locate memory to a specific brain area. He concluded with the theory of “mass action” that basically states: “the amount of lost memory was proportional to the amount of cortex removed (lesioned)”. He consequently theorized that any specific functional area of the cortex could be replaced by another one. He called this equipotentiality.
1.4.2 First evidence of the physical location of memory in the brain
Parallel to Lashley’ lesional studies using animal models, other researchers reported different observational studies in humans related to memory functions.
One of the very probably first evidence that memory was located in the medial temporal
lobe (MTL) of the cerebral cortex came with the invention, in the 1930s, of the Montreal procedure developed by the American-Canadian neurosurgeon Wilder Graves Penfield (1891-1976). Developed for the surgical treatment of patients with epileptic seizures, brief electrical stimulations were applied to different brain areas on conscious patients. The responses were observed and epileptic areas were identified for subsequent resection.
Penfield reported that stimulations of MTL lead to the recall of vivid memories in some patients (Milner, 1977). These findings have been replicated by modern surgeons (Bartolomei et al., 2004).
Maybe the most famous evidence of the possible location of memory in the brain emerged with the studies of Scoville and Milner (1957) on patients whose MTL were resected because of psychoses and intractable seizures. Although in their original report, Scoville and Milner reported at least ten different patients with differently severe memory defects and with different bilateral MTL resections, particular attention has been paid to patient Henry Molaison (H.M.). H.M. developed epileptic seizures one year after he fell off a bicycle when he was 9 years old. At the age of 16 H.M. seizures became untreatable with drugs and at the age of 27 he underwent bilateral medial temporal lobectomy including the removal of both hippocampi. The consequences of this intervention were immediately clear, H.M. developed anterograde amnesia, the inability to form new episodic memories. He also presented graded retrograde amnesia, the inability to recall memories closed in time to the surgery. Surprisingly, H.M. could recall old memories, for example he could remember his childhood or his university years. His working memory was intact as well as his ability to form long-term procedural memories like learning new motor skills despite him not being conscious of learning them.
Around the same period and in the following years some other examples of human memory impairment cases appeared and were studied experimentally. One of these studies was reported by Stuart Zola-Morgan in 1986 and concerned R.B. patient who after an ischemic episode developed memory impairment (Zola-Morgan et al., 1986). As it reported in the study, in his last 5 years before death, R.B. formal memory tests demonstrated considerable anterograde amnesia but not retrograde amnesia or any sign of other cognitive deficits. Post-mortem histological investigation revealed bilateral lesion of specific subregions called CA1 fields (see next chapter). This case has been one of the first where lesions were determined by extensive and detailed neuropsychological and neuropathological examination and were limited to the hippocampus.
Altogether, these studies give us an extraordinary insight on the brain mechanisms of memory. Damage to the MTL is sufficient to cause episodic memory impairment and the
severity of memory impairment strongly depends on the locus and the extent of the damage. These human studies indicate a central role of the MTL in human episodic memory.
2. The anatomy of the hippocampus
2.1 The medial temporal lobe (MTL) is composed of the hippocampus (HPC) and Brodmann areas 35-36
Evidence from clinical studies attribute to the MTL an essential role for correct memory function, at least for episodic memories. Defining its anatomy and connections is thus essential. Because the present work will mainly focus on mice’s memory I will here develop the anatomical profile of the rodent’s hippocampal formation. Most of the anatomical descriptions that will follow in the text were elaborated and extracted from “The Hippocampus book” (Per Andersen, 2007) as it is one of the most detailed and complete anatomical work on the hippocampus (human and rodent). The MTL includes the hippocampus (HPC) and Brodmann areas 35 (A35) and 36 (A36). The HPC is divided into the hippocampal formation (HF) and the parahippocampal region (PR). The HF is composed of subregions: dentate gyrus (DG), Cornu Ammonis (CA) and subiculum. The PR consists of: medial and lateral entorhinal cortex (MEC and LEC), presubiculum (PrS) and parasubiculum (PaS) (Figure 3).
In rodents, the HPC is located in the caudal part of the brain and has a c-shaped structure (Figure 3 A). The HPC is composed of different relay-areas that specifically encode spatial information, emotions, olfaction and memory (Figure 3 B). These areas are: DG, the CA areas (CA3, CA2 and CA1 ordered by information flow) and the subiculum. The HPC has well defined cortical layers. The HF appears in a three-layered structure for each of its regions while the PR has a six-layered structure (Figure 3 B).
Figure 3. Anatomy of the rodent’s hippocampus, adapted from Moser et al. (2014) and Van Strien et al., (2009).
A. Mid-sagittal view of the rodent hemisphere showing hippocampal and parahippocampal positions. The dashed line indicates the dorsoventral position of the section. B. Horizontal cross section (Nissl-stained) of medial temporal lobe (DG, CA3, CA1, Subiculum, PrS, PaS, MEC, LEC, A35, A36). Cortical layers are shown by Roman numerals and the position of each layer is indicated. Figure legend: DG, dentate gyrus;
CA, cornu ammonis; PrS, presubiculum; PaS, parasubiculum; MEC, medial entorhinal cortex; LEC, lateral entorhinal cortex; A35, Brodmann area 35; A36, Brodmann area 36; Dist, distal; encl, enclosed blade of the DG; exp, exposed blade of the DG; gl, granule cell layer; luc, stratum lucidum; ml, molecular layer; or, stratum oriens; prox, proximal; pyr, pyramidal cell layer; rad, stratum radiatum; slm, stratum lacunosum- moleculare.
In the DG, the first layer is called hilus (often referred as polymorphic cell layer) containing both afferent and efferent fibers as well as glutamatergic neurons (mossy cells) and ɣ- aminobutyric acid expressing cells (GABAergic). On top of this layer we find the granule cell layer, which is mostly composed of granule cells (GCs) as the name states it. Granule cells have an elliptical cell body with a diameter of around 10 μm (Claiborne et al., 1990).
The rodent granule cell layer of the DG contains approximately 1.2x106 GCs that are densely compacted (Rapp and Gallagher, 1996). This layer is one of the few brain region where we have neurogenesis continuously producing new granule cells throughout adulthood (Cameron and McKay, 2001). Superficially to this layer, we have the molecular layer, stratum moleculare that is a mainly free-cell body layer, principally composed of dendrites from granule cells and interneurons (INs). Together, the granule cell layer and the molecular layer form a u-shaped structure (Figure 3 B).
Cornu Ammoni Areas
The CA areas are divided into three main subregions: CA3, CA2 and CA1 as firstly described by Lorente de Nó in 1934. These regions connect the DG with the subiculum and are highly interconnected through densely packed pyramidal neurons. The deeper CA
layer, stratum oriens, contains the basal dendritic trees of pyramidal cells and different interneurons. Superficial to the stratum oriens layer, lays the stratum pyramidale, which correspond to the granule cell layer in the DG. It is principally composed of pyramidal cells (the bodies), which are the main excitatory cells of the hippocampal formation, as well as different types of cell body interneurons. The stratum radiatum is composed of apical dendrites and some interneurons. The most superficial layer of the CA regions is the stratum lacunosum-moleculare containing the very apical tufts of the apical dendrites from pyramidal cells. Although this three-layered structure is found in all the CA regions of the hippocampal formation, the CA3 region has one additional layer compared to the other CA areas, called stratum lucidum where DG cells send their fibers (mossy fiber projections).
The subiculum, located immediately at CA1 border, has a similar cytoarchitecture as the CA areas (O'Mara et al., 2001). The main difference in the subiculum is that the stratum radiatum of CA1 is replaced by the stratum moleculare that physically merge in a superficial and deeper manner the two layers of CA1, the stratum radiatum and the stratum lacunosum-moleculare of CA1. Another difference is that in the subiculum we do not find the stratum oriens. The subiculum is principally composed of large pyramidal neurons but are less tightly packed as in CA1 (Funahashi and Stewart, 1997; Harris et al., 2001). Particularly, two different pyramidal cells have been described based on their physiological profile: intrinsically bursting cells and regular spiking cells (Greene and Totterdell, 1997). Regular spiking cells are found mostly in the superficial pyramidal cell layer while bursting cells are found more in the deeper part of the same layer. Together, large pyramidal neurons are projecting neurons but it seems that only bursting cells project to the EC. The subiculum is populated by several types of INs similarly to the CA areas.
The entorhinal cortex (EC) is the major input and output structure of the hippocampal formation. The EC is generally divided into two main regions: the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC). MEC and LEC provide different information to the HF: the MEC is commonly considered to provide spatial input to the HF while the LEC provides non-spatial information.
The architecture of the EC is generally defined in reciprocally connected six layers.
All the entorhinal layers are reciprocally connected. Layer II contains pyramidal and stellate cells that project to the DG and CA3, giving birth to the perforant pathway. LEC
23 layer II pyramidal cells group into islands and directly project to CA1 (Kitamura et al., 2014). Layer III contains manly pyramidal cells that also project to CA1 and subiculum.
2.2 Hippocampal connectivity: within and between brain regions
Within the hippocampal formation, DG and the CA regions form a trisynaptic circuit first described by Santiago Ramon y Cajal (Figure 4). The circuit forms a loop where information through principal glutamatergic cells travels from the EC to DG, from DG to CA3 and from here to CA1 through synaptic transmission between principal cells of these regions. The classical description consists of three major projections: 1) The Layer II neurons form the entorhinal cortex transmit polymodal sensory information by projecting and making synapses with granule cells dendrites in the DG via the perforant path. 2) Granule cells project their axons, the mossy fibers, to CA3 where the contact dendrites of pyramidal cells. 3) CA3 project ipsilaterally to pyramidal cells of the CA1 via Shaffer collaterals and contralaterally through commissural fibers that contact CA3 and CA1 pyramidal cells. The trisynaptic pathway of the hippocampus is one of the most studied region over the last two decades. It is the major brain site used to study synaptic plasticity mechanisms and memory processes (developed in chapter 4).
Figure 4. Basic circuits of the hippocampus, adapted from Neves et al. (2008).
The diagram shows the traditional trisynaptic pathway. The perforant path conveying polymodal sensory information from entorhinal cortex layer II neurons to the dentate gyrus carries out the main input to the hippocampus. Perforant path axons make excitatory synaptic contact with granule cells’ dendrites: axons from the lateral and medial entorhinal cortices innervate the outer and middle third of the dendritic tree, respectively. Granule cells send their projections (the mossy fibers), to the proximal apical dendrites of CA3 pyramidal cells. CA3 principal cells project to ipsilateral CA1 pyramidal cells through Schaffer collaterals and to contralateral CA3 and CA1 pyramidal cells through commissural connections. Additionally, there is also an important associative network interconnecting CA3 cells on the same side. CA3 pyramidal cells are also innervated by a direct input from entorhinal cortex layer II neurons (not shown). The distal apical dendrites of CA1 pyramidal neurons receive a direct input from entorhinal cortex layer III neurons. There is also substantial modulatory input to hippocampal neurons.
Hippocampus Entorhinal cortex
Nature Reviews | Neuroscience CA3
II Perforant path to CA1
Polymodal sensory information
Modulatory input Mossy fibres
Perforant path to dentate gyrus Associational/
commissural fibres Schaffer collaterals
‘neurophysiological postulate’ proposes that connections between co-active neurons are strengthened through mechanisms of synaptic plasticity, so that subsequent activation by incoming stimulation of only a sub-component of the assembly will lead to activation of the whole assembly, thereby recapitulating the activity elicited by the original event. (LTP is a Hebbian process, since its induction requires coincident activity of the pre- and postsynaptic neu- rons.) The immediate problem is to identify such cell assemblies in the hippocampal encoding of memory.
Single-unit recordings from neurons in the hippocampus of freely moving rodents reveal that pyramidal and granule cells show a preference for firing in a particular loca- tion of an explored environment, regardless of the direction from which the animal enters the location33(BOX 1). Hundreds of such ‘place cells’ fire in concert as a rat reaches a particular location, and place cells fire in sequence as the animal moves Synaptic plasticity in the hippocampus
The hippocampus has been a major experimental system for studies of synaptic plasticity in the context of putative informa- tion-storage mechanisms in the brain. Its simple laminar pattern of neurons and neural pathways (FIG. 1) enables the use of extracellular recording techniques to record synaptic events for virtually unlimited peri- ods in vivo12. The much-studied model of synaptic plasticity, long-term potentiation13,14 (LTP; see FIG. 2a), was first identified in the hippocampus and has been extensively characterized using electrophysiological, biochemical and molecular techniques15. Several recent studies have detected LTP- like synaptic changes in the hippocam- pus16,17(FIG. 2b) and the amygdala18 following learning. Other forms of activity-dependent plasticity have been found, including long-term depression (LTD)19, EPSP-spike (E-S) potentiation20,21, spike-timing-dependent plasticity (STDP)22, depotentiation23–25 and de-depression25,26. The transverse hippo- campal slice preparation27(FIG. 2a) has been of major importance to this field, enabling
pharmacological agents to be rapidly washed on and washed off and allowing intracellular and patch-clamp recordings.
In addition, hippocampal neurons can be cultured28,29, either as transverse ‘organo- typic’ slices or as populations of dissociated neurons, for periods of months, facilitating molecular manipulations such as over- expression or RNAi-based knock-down of specific proteins. These in vitro techniques have greatly enhanced our understanding of the molecular mechanisms that underlie synaptic plasticity15,30. In the hippocampus it has been possible to track effects such as the phosphorylation of a protein at a specific residue at multiple levels of organization, from isolated synaptic membranes all the way through to the behavioural analysis of intact animals with specific molecular defects31. Nevertheless, the larger picture of how synaptic plasticity in extensive networks of cells leads to the storage and recall of information remains dimly illumi- nated. The Canadian psychologist Donald Hebb posited a role for such assemblies as engrams or memory traces32. His famous Figure 1 | Basic anatomy of the hippocampus. The wiring diagram of the
hippocampus is traditionally presented as a trisynaptic loop. The major input is carried by axons of the perforant path, which convey polymodal sensory information from neurons in layer II of the entorhinal cortex to the dentate gyrus. Perforant path axons make excitatory synaptic contact with the dendrites of granule cells: axons from the lateral and medial entorhinal cortices innervate the outer and middle third of the dendritic tree, respec- tively. Granule cells project, through their axons (the mossy fibres), to the proximal apical dendrites of CA3 pyramidal cells which, in turn, project to ipsilateral CA1 pyramidal cells through Schaffer collaterals and to contra- lateral CA3 and CA1 pyramidal cells through commissural connections. In
addition to the sequential trisynaptic circuit, there is also a dense associa- tive network interconnecting CA3 cells on the same side. CA3 pyramidal cells are also innervated by a direct input from layer II cells of the entorhinal cortex (not shown). The distal apical dendrites of CA1 pyramidal neurons receive a direct input from layer III cells of the entorhinal cortex. There is also substantial modulatory input to hippocampal neurons. The three major subfields have an elegant laminar organization in which the cell bodies are tightly packed in an interlocking C-shaped arrangement, with afferent fibres terminating on selective regions of the dendritic tree. The hippocampus is also home to a rich diversity of inhibitory neurons that are not shown in the figure. For a full description of hippocampal anatomy, see REF. 90.
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The major input of the hippocampal formation is from the EC. In 1893, Ramón y Cajal first described projections from the entorhinal cortex, in particular layer II, to the dentate gyrus and named it the perforant pathway. Layers III, V and VI also project to the hippocampal formation but to a lesser extent. These projections also target the CA fields and the subiculum, the temporoammonic pathway. Perforant path terminals specifically target the apical dendrites and form synapses with principal cells and interneurons in the molecular layer of the DG or the stratum lacunosum-moleculare of CA3 (Nafstad, 1967; Hjorth- Simonsen and Jeune, 1972). Thus, subsets of cells that do not have dendrites in this specific layer are not innervated by these projections. This is particularly the case of hilar mossy cells that receive little or no perforant input. The MEC and LEC projections share similar anatomical targets though not completely identical. In particular, layer II of LEC projections target the outer third of the molecular layer of the DG, while MEC project more to the middle third of the same layer. Similarly, layer II of MEC projects deeper in the stratum lacunosum-moleculare of the CA3 whereas LEC projects more superficially in this layer. Another difference is that layer III of MEC projects to the proximal stratum lacunosum-moleculare of CA1 and distal molecular layer of subiculum while the same layer of LEC project to distal CA1 and proximal subiculum.
DG projects to the CA3 via granule cells axons, the mossy projections. Mossy and GABAergic cells of the hilus project their axons to the molecular layer of both ipsilateral and contralateral DG. The DG receive different projections from different brain areas, but there is no evidence for DG to project outside of the CA areas. The major input to the DG is the projection form the layer II of the EC, the perforant pathway. DG receive septal projections arising from the medial septal nuclei (Mosko et al., 1973; Baisden et al., 1984);
(Amaral and Kurz, 1985; Wainer et al., 1985; Nyakas et al., 1987). Septal projections target the dentate gyrus closely to the granule cells layer and principally arise from the medial septal nucleus and the diagonal band of Broca. Most of these projections are cholinergic, but some are GABAergic. This heterogeneity matches the anatomical segregation where septal GABAergic cells projects preferentially to other GABAergic cells of the dentate gyrus whereas septal cholinergic cells mainly project to the molecular layer of the dentate gyrus where they contact dendrites of the granule cells. Interestingly, only around 5-10% of cholinergic projections contact interneurons. Other inputs to the molecular layer of the DG arise from the hypothalamus, specifically from the glutamatergic
neurons of the supramammilary area (Wyss et al., 1979; Vertes et al., 1993; Magloczky et al., 1994; Kiss et al., 2000). Also, the noradrenergic system from the locus coeruleus of the pons terminates in the polymorphic layer (Swanson and Hartman, 1975; Loughlin et al., 1986). DG also receive dopaminergic projections from ventral tegmental area (VTA) as well as GABAergic projections (Ntamati and Luscher, 2016). Another mid brainstem projection is the serotonergic system originating from the raphe nuclei that target the DG (Moore and Halaris, 1975; Kohler and Steinbusch, 1982; Vertes et al., 1999). Most of cells targeted by this input are GABAergic interneurons, basket cells as well as calbinding positive cells. There is significant intrinsic connectivity within the DG. This connectivity occurs mainly between principal granule cells and different types of glutamatergic and GABAergic cells giving rise to the different form of activity further described in chapter 5 (feedforward inhibition, feedback inhibition and disinhibition).
CA3 predominantly projects to CA1 principal cells, through the Shaffer collaterals. CA3 receives projections from the DG mossy fibers. The classical view is that CA3 is highly innervated by its own collateral axons (associational connectivity) and receive also contralateral CA3 connections (commissural connectivity). However, this view has been recently challenged by the study of Guzman et al. where they showed, by using electrophysiological ex-vivo slice recordings, that CA3 associational connectivity is rather sparse (Guzman et al., 2016). There is evidence for pyramidal neurons of CA3 to project back to the DG molecular layer (Laurberg, 1979; Li et al., 1994). The noradrenergic system from the locus coeruleus of the pons projects to the stratum lucidum of CA3 (Pickel et al., 1974; Swanson and Hartman, 1975; Loughlin et al., 1986). The basal nucleus of the amygdaloid complex mostly sends its projections to stratum oriens and radiatum of the CA3 (Pikkarainen et al., 1999; Pitkänen, 2000). The cholinergic projection from the septum also targets the CA3. CA3 bilaterally projects back to the lateral septal nucleus via the fimbria.
CA1 main inputs come from the CA3 projections (Shaffer collaterals) and the EC layer III, (temporoammonic pathway). The nucleus reuniens of the thalamus sends mainly glutamatergic projections to the stratum lacunosum-moleculare of CA1 where they contact apical dendrites from pyramidal neurons and GABAergic cells (Herkenham, 1978;
Dolleman-Van der Weel and Witter, 2000). Principal cells of CA1 project to a variety of
different regions comprising the subiculum, the entorhinal cortex, the perirhinal cortex, the retrosplenial cortex, the medial temporal lobe, the amygdaloid complex, the septal nucleus. Proximal principal CA1 pyramidal cells project to the MEA while distal pyramidal cells connect to the LEA. Connectivity to the amygdaloid complex consist of projections in order of its importance to the basal nucleus, the amygdalohippocampal area and all the nuclei including the lateral, central, accessory basal, medial and posterior cortical (Pitkänen, 2000). Differently form CA3 principal cells, CA1 pyramidal neurons have weak associational projections. Interestingly, evidence revealed that CA3 receive backprojections form the CA1 inhibitory cells found in the stratum oriens and radiatum (Laurberg, 1979; Amaral et al., 1991; Swanson et al., 1981).
3. Functional roles of hippocampus
3.1 Hippocampal function
Several different functional roles have been attributed to the hippocampus. Penfield and Erickson studied the involvement of the hippocampus in olfaction (Penfield, 1942). In 1937, James Wanceslas Papez (1883-1958) proposed that the hippocampus was involved in emotion processing and proposed the “Papez circuit” (Papez, 1995). Although the identification of the functional role of the hippocampus took researchers occupied for decades (we will focus on this topic in the following chapters), the study of the anatomy took its own way. The first anatomist who described the hippocampus was Julius Caesar Aranzi (1530-1589) in 1564. Aranzi named it hippocampus because of its similarity to the tropical fish Seahorse (Per Andersen, 2007).
Today, one of the most studied function of the HPC is spatial memory and representation of the environment. Edward Tolman (1948) first advocate the idea of the existence of a cognitive map in the brain able to represent the space (Tolman, 1948).
Twenty years later, O’Keefe and Dostrovsky in 1971 first described place cells in the CA1 field of the anterior dorsal hippocampus of rats. They demonstrated, by combining electrophysiological recordings with animal behavior, that cells in the hippocampus of the rat increased their firing activity (preferential firing) when the animal was in a specific location of the environment (its place field), they named these cells place cells (O'Keefe, 1976). This discovery conveyed O’Keefe, together with May-Britt and Edvard Moser, the Nobel Prize in Medicine in 2014. There is also evidence of the existence of place cells in humans (Ekstrom et al., 2003). Different place cells have different place fields, which have been reported to be stable across recording sessions in the same environment. As reported by different studies, place cells are principal cells of CA1, CA3 and DG but there
is also evidence for cells having similar functions in the subiculum (Kjelstrup et al., 2008;
Sharp and Green, 1994; Sharp, 1997). One characteristic of place cells is that their position in the dorsoventral axis determines the size of the place field they code for. Their place field increases from the dorsal to the ventral axis. Another property of place cells is that variations in the environment can influence their firing rates (Anderson and Jeffery, 2003).
More recently, Marianne Flynn together with colleagues in the laboratory of May-Britt and Edvard Moser (Fyhn et al., 2004) described another very interesting characteristic for spatial representation in the brain, the grid cells in the medial entorhinal cortex (MEC).
Grid cells were lately observed also in the presubiculum and parasubiculum (Boccara et al., 2010). Grid cells, like place cells, fire in a specific location of the environment but they present multiple firing fields organized in hexagonal grid-like configuration. Importantly, while place cells may differentially codes multiple contexts, grid cells maintain the positional relationships between distinct environments. Some properties of grid cells have been described: the scale between the firing fields, (distance), the orientation of grid axes according to direction references and the spatial phase. Moreover, grid cells also show differential tuning of fields’ size along the dorsoventral axis of the MEC like place cells.
Altogether, these evidences show that the HPC has a functional role in spatial navigation, representation of the environment and the formation of cognitive maps.
3.2 The role of the hippocampus in memory
As seen in chapter 1.2, clinical studies have shown evidence that the HPC is a core structure for episodic memory. Today, the HPC is known to play fundamental roles in the formation, retention, recall and extinction of episodic memories (Per Andersen, 2007). To precisely define the role of HPC in memory, researchers have used animal models with a huge diversity of approaches which include: experimental memory tasks together with lesional studies (Aggleton and Pearce, 2001), the use of toxins (Jarrard, 1989) and drugs (McGaugh and Izquierdo, 2000) to alter cells activity and genetic approaches (Tonegawa et al., 1995).
These studies led researchers to propose models explaining hippocampal function. We have models suggesting that sensory information from the entorhinal cortex to the dentate gyrus is encoded into CA3 auto-association networks and can be subsequently retrieved via Shaffer collateral activation of CA3-CA1 synapses (Rolls and Kesner, 2006; Nakashiba et al., 2008; Wang and Morris, 2010). Precisely, the learning-induced memory trace is thought to be encoded by the occurrence of plasticity in CA3-CA3 auto association circuits