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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
INSTITUTE OF BIOLOGICAL SYSTEMS AND GENETICS RESEARCH
IBRAHIM KAZMA
PLACE AND GRID CELLS: STRUCTURAL ARRANGEMENT AND ROLE IN MEMORY FORMATION AND SPATIAL MAPPING
Final Master's Thesis
Thesis Supervisor Assoc. Prof. Dr. Arūnas Bielevičius
Kaunas, 2020
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TABLE OF CONTENTS
1. SUMMARY ... ... 3
2. CONFLICTS OF INTEREST... ... 5
3. ABBREVIATIONS ... ... 6
4. INTRODUCTION ... 7
5. AIMS AND OBJECTIVES ... 8
6. LITERATURE REVIEW ... 9
6.1 Introduction ...9
6.2 Place Cell Function and Distribution... 14
6. 3 Grid Cell Function and Distribution ... 24
6.4 Place Cell and Grid Cell Interaction ... 32
6.5 Place Cell and Grid Cell Role in Memory Formation... 34
7. Methodology and Methods …... 40
8. Results and Discussion …... 41
9. Conclusion …... 44
10. References …... 45
11. Appendix …... 48
Appendix A …... 48
Appendix B …... 51
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1. SUMMARY
Ibrahim Kazma
Place and Grid cells: Structural Arrangement and Role in Memory Formation and Spatial Mapping
In 1971, O’Keefe and Dostrovsky first described a cell type in the hippocampus that participates in spatial mapping called the place cell. These cells were found in the hippocampus and respond in specific “place fields”. This discovery then motivated many other researchers to further reveal the entire mechanism behind spatial mapping; this led to the discovery of what are now called grid cells in 2005 by Hafting et al. They described a subset of neurons in the medial entorhinal cortex (MEC) that fired in a grid-like pattern which divides the location the organism currently inhabits.
These discoveries were accompanied by the discovery of other types of cells in neighboring regions of the brain that functioned in a manner that compliments these cells like boundary cells, head direction cells, and velocity cells among others [1]. Even early on, it became apparent that these cell types had connections to memory establishment, but that relationship was not fully clarified or detailed. The purpose of this literature review is to highlight existing knowledge on place and grid cell distribution and function, as well as their role in memory formation.
Aims: This paper aims to analyze the structure and organization of the firing fields of place and grid cells followed by their contributions to spatial mapping with possible avenues of future research.
Objectives:
1.To detail firing characteristics of place and grid cells 2.To present topological features of these cells
3.To analyze their contributions to spatial mapping in the brain 4.To explore their role in episodic memory formation
5.To highlight future avenues of research
Methodology: Entry of “place cells” and “grid cells” in combinations with “function”, “memory”,
“physiology”, and “neuroanatomy” into relevant medical databases then selecting the most relevant and well-cited (impactful to the field) research papers from researchers who have a significant body of work in the field when possible.
Results: Many parts of the system through which place and grid cells operate have been described (to varying degrees), but many questions (some foundational) still remain to be answered, and with modern technology, with high sensitivity and specificity, this may become a possibility.
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2. CONFLICTS OF INTEREST
No conflicts of interest to report.
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3. ABBREVIATIONS
amHipp – Anteromedial hippocampus DG – Dentate Gyrus
EEG – Electroencephalogram ECIII– Entorhinal cortex layer III EC – Entorhinal cortex
IMPCs – Intermediate pyramidal cells IMSCs – Intermediate stellate cells LTP – Long-term potentiation
MECIII – Medial entorhinal cortex layer III mEC – Medial entorhinal cortex
PCs – Pyramidal cells SCs – Stellate cells
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4. INTRODUCTION
Initially described in 1976, place cells were a revelation in terms of understanding how the brain perceived its surroundings. This was followed by the discovery of grid cells in 2005 in an attempt to understand the entire circuit they are parts of. There has been a continuous stream of research into these cell types since the late 1970s up to the present day since shedding a light on this circuit might help understand how other neighboring circuits operate, and with their location being the medial temporal lobe (hippocampus for place cells, entorhinal cortex for grid cells), there are a lot of vital function in relation to sensation and perception that can be potential better understood. In this setting, O’Keefe and the Mosers won the Nobel prize in Physiology or Medicine in 2014 which resulted in a renewed interest in the field and these two cell types in particular; their physiology, anatomy, and function became renewed topics of interest, as well as the burgeoning understanding of their role in the formation and contextualization of episodic memories. Theoretically, an understanding of how place and grid cells operate and interact is crucial to understanding the processes underlying spatial perception in the brain; practically, this understanding helps explain and pinpoint causes of spatial perception dysfunction, as well as to estimate potential deficits in brain lesions. In order to collect and succinctly itemize this data, a literature review based on an analysis of the research to this point is most appropriate. Thus, in this review, I will detail the structure and function of place and grid cells, highlight how they interact, and point out their role in memory formation while also directing into future avenues of research to help find answers to outstanding questions that yet remain in the field.
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5. AIMS AND OBJECTIVES
This paper aims to analyze the structure and organization of the firing fields of place and grid cells followed by their contributions to spatial mapping with possible avenues of future research highlighted.
Objectives:
1.To detail firing characteristics of place and grid cells 2.To present topological features of these cells
3.To analyze their contributions to spatial mapping in the brain 4.To explore their role in episodic memory formation
5.To highlight future avenues of research
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6. LITERATURE REVIEW
6.1 Introduction
In the exploration of these cell types, it would be useful to go into more detail as it relates to the hippocampus and entorhinal cortex themselves as brain structures to better understand the environment place and grid cells function and interact in.
The hippocampus is a bilaminar structure in the medial temporal lobe that occupies the medial region of the wall of the lateral ventricle consisting of two grey matter folds: the cornu ammonis (hippocampus proper) and the dentate gyrus (Fig.1). It can be divided into 3 parts in the axial and sagittal planes: anterior segment (Head), intermediate segment (Body), and posterior segment (Tail). The cornu ammonis can be further divided functionally into four Sommer sections CA1-4 [2]. The anterior segment consists of the subiculum and CA1 section and the posterior section mostly consists of the dentate gyrus and CA2-3 (Fig.2) [3].
10 Fig. 1.
Anatomy of the hippocampal formation on 3-T axial T2 (a) and sagittal 3D-MPRAGE images (b) Zoomed-in 3-T coronal T2-weighted images at the level of the hippocampal head (c) and the hippocampal tail (d). The hippocampal body is shown in detail in Fig. 2. 1 = hippocampal head, 2 = hippocampal body, 3 = hippocampal tail, 4 = mesencephalon, 5 = amygdala, 6 = hippocampal digitations, 7 = temporal horn of the lateral ventricle, 8 = uncal recess of the lateral ventricle, 9 = splenium of the corpus callosum, 10 = subsplenial gyri, 11 = crura of the fornices [2]
Fig. 2.
Anatomy of the hippocampal formation at the level of the hippocampal body on 3-T coronal T2 The hippocampal formation consists of the cornu ammonis or hippocampus proper, which can histologically be divided into the four Sommer sectors CA1–CA4, and the dentate gyrus (DG). A small hippocampal cyst (Hs) reflects the location of the largely obliterated hippocampal sulcus. A = alveus, Ac = Ambient cistern, B = basal vein of Rosenthal, C = tail of caudate nucleus, ChF = choroid fissure, CS = collateral suclus, DG = dentate gyrus, P = posterior cerebral artery, PHG = parahippocampal gyrus, Sub = subiculum, T = temporal horn of the lateral ventricle, Tb = transverse fissure of Bichat [2]
CA1 and the subiculum are the main output regions of the hippocampus, and thus, many of the circuits in the hippocampus end in either (mostly CA1). In the CA1 region, there are 3 main pathways: the trisynaptic pathway (The trisynaptic pathway is a pathway consisting of three neurons: granule cells in the DG, pyramidal cells in CA3, then pyramidal neurons in CA1) through Schaffer collaterals from the dentate gyrus and CA3 to the proximal apical dendrites of pyramidal cells CA1 in stratum radiatum, the direct pathway from layer III of the entorhinal cortex to the distal apical dendrites of the pyramidal cells in CA1 in stratum lacunosum moleculare, and CA2 direct links to the basal dendrites of pyramidal cells in CA1 in stratum oriens (Fig.3). Each of these pathways has its function indicated by the varied consequences of their interruption [4].
11 Fig.3.
Hippocampal CA1 in relation to other subregions: subiculum (S), CA2, CA3, dentate gyrus (DG) CA1 pyramidal neuron is indicated with labeled strata: stratum oriens (SO), stratum radiatum (SR) and stratum lacunosum moleculare (SLM). B) CA1 pyramidal neuron excitatory (triangle) and inhibitory (circle) inputs to different parts of its apical dendrite in SR, SLM and basal dendrite in SO. Distal apical dendrite receives direct input from the entorhinal cortex (EC) and nucleus reuniens of the thalamus (nRT).
Indirect EC input arrives at the proximal apical dendrite, via DG and CA3, or at the basal dendrite, from CA2. Each pathway can elicit inhibition in feedforward fashion [4]
Furthermore, the CA1 region is divided into spatial and non-spatial regions on three bodily axes:
transverse (proximo-distal), radial (deep-superficial), and longitudinal (dorsal-ventral). The areas related to spatial analysis are proximal, deep, and dorsal (Fig.4) [4].
Fig.4.
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Across transverse axis, spatial encoding is more robust towards CA2 (proximal); non-spatial encoding is more robust towards S (distal)
Across the radial axis, deep neurons (blue triangle) show more spatial tuning and superficial neurons (black triangle) appear to be specialized for non-spatial processing. B. Across the longitudinal axis, dorsal CA1 (top) shows more robust spatial responses whereas ventral CA1 (below) is specialized for various affective and motivational behaviors [4]
As for the entorhinal cortex (EC), it is a structure embedded in the anterior temporal lobe forming what can be considered a relay station in the medial temporal lobe. It is also divided into two different subfields that have different connectivity patterns with surrounding structures (Fig.5) that help facilitate a variety of functions related to memory and perception [5]. These two subfields are the posterior-medial and the anterolateral fields with grid cell patterns of firing located in the medial EC (mEC) (Further illustration of this division and varying connectivity is in Appendix A)
Fig. 5.
Anatomical connectivity studies in animals and functional connectivity studies in humans converge Bilateral connections convey spatial information between dorsal visual regions, parahippocampal cortex (PHC), posterior-medial entorhinal cortex (EC), and hippocampus (HC), and object information between ventral visual regions, perirhinal cortex (PRC), anterolateral EC, and HC. Importantly, these parallel circuits are interconnected on the level of PRC-PHC and EC; and some connections skip levels. Not depicted are differential connectivity patterns between EC subregions and HC subfields, and intrinsic connections within subregions. A, anterior; P, posterior; L, lateral; M, medial [5]
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Grid cells are located in layer II of the mEC which is divided into calbindin and reelin-expressing cells where the calbindin-expressing cells are in patches superficial to the reelin-expressing cells. The calbindin-expressing cells are the pyramidal cells (PCs) and intermediate pyramidal cells (IMPCs) while the reelin-expressing cells are stellate cells (SCs) and intermediate stellate cells (IMSCs) (Fig.6). These reelin-expressing cells project into the dentate gyrus and CA3 regions of the hippocampus and help form the trisynaptic circuit for spatial perception [6].
Fig.6.
Layer II cells come in two, chemically defined types, reelin- and calbindin-positive
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(A) Coronal sections were taken through the entorhinal cortex (EC) of the rat (left) and mouse (right), stained for reelin (cyan) and calbindin (magenta). Note the different positions of the two cell populations in the two species and the two subdivisions of EC. In the rat medial EC (MEC; top-left), the two populations are intermingled with a tendency for both types to cluster somewhat. In contrast, in mouse MEC (top-right), calbindin-positive cells form clear clusters (white arrowheads) that are located superficial to the reelin-positive neurons. In lateral EC (LEC) of the rat (bottom-left), reelin-positive cells form superficially positioned clusters (white arrowheads), separated by calbindin-positive dendritic bundles belonging to the deeper positioned, equally dispersed calbindin-positive neurons. In LEC of the mouse (bottom-right), a more equal distribution is seen, although two superficially located reelin clusters are present (white arrowheads). Scale bars equal to 100 μm. (B) Schematic representation of the relationships of morphologically, electrophysiologically and connectionally defined cell types, and their chemical phenotype in LEC and MEC. Abbreviations: Fan, fan cell; IMSC, intermediate stellate cell;
IMPC, intermediate pyramidal cell; multi, multipolar cell; ObPC, oblique pyramidal cell; PC, pyramidal cell; SC, stellate cell [6]
6.2 Place Cell Function and Distribution
Place cells are a type of cell distributed in the hippocampus that respond to the animal’s current location with different firing fields that are divided nontopographically and with varying place field sizes [7]. Place cells that determine location are in the CA1 and CA3 regions of the hippocampus primarily and anterior (ventral) place cells have firing fields that cover a larger area of space than posterior (dorsal) place fields [3]. Both these field sizes have different functions in pattern completion (surrounding identification) and pattern separation (distinguishing different surroundings).
Concerning place cell firing fields, it is still unclear how place cell neural activity codes information; there are 2 main theories: the local or dedicated-coding hypothesis where one finely-tuned
“cardinal” cell (a place cell in this case) independently detects specific stimuli, and the distributed or ensemble-coding hypothesis where the encoded information is distributed between cells. Each cell is active in multiple representations, and therefore cannot independently signal information unambiguously.
A unique across-cell ensemble pattern of discharge defines a specific representation [8]. The experiment in [8] showed that the latter hypothesis is more likely since it showed that each place cell had multiple, independent place fields that varied ambiguously depending on the size of the environment the experimental animal was in (Fig.7) while also highlighting a flaw in some experiments in that the experimental environments in a lot of them were too small (<1m) for place fields to exhibit more than one field, or for all of them to activate (more place cells were stimulated in the larger environment than in the smaller one). This ambiguity in firing is mirrored in place cell remapping where place cells are stimulated
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in 2 different environments in ways that are random meaning that they are part of an ensemble of place cells that encode location together but not independently. The first hypothesis is thus capable of explaining only the representation of single locations within an environment since each place cell has one field for one location in an environment even if the total environment is coded as an ensemble.
Another point that became clear is that field size doesn’t change in proportion to the environment or even previously detected place fields. Furthermore, another interesting point that [8] pointed out is that when other animals were put in a similarly-sized environment, there was variation in results (Their experimental animal was the rat but they pointed out another experiment where monkeys showed one place field in the same environment). This shows one of two things: either the hippocampus codes differently in different species (which seems unlikely) or that place field size differs among species;
another more expanded experiment may be needed to confirm one or the other.
16 Fig. 7.
More pyramidal cells were recruited to be place cells in the chamber than the cylinder
The rats were put in two concentric containers that can be opened to each other. A–C, An example cell from each rat. These cells rarely fired and did not have place fields in the cylinder. D, Proportion of pyramidal cells that were place cells in the two environments. Probabilities are given for comparisons to the cylinder recordings [8]
Fig. 8.
17 Basic place cell characteristics were similar on the chamber and cylinder floors.
A–C, Overall firing rate (A), spatial information content (B), and local smoothness (coherence) (C) of place cell firing in the cylinder and chamber. Probabilities are given for significant differences relative to the cylinder condition. The significant differences arise because discharge on the narrow stairs in the chamber was different from discharge on the cylinder or chamber floors. Error bars indicate SEM [8]
From this, it can be concluded that an overlap of multiple place fields (termed place cell co- firing) as an animal moves in an environment helps the brain determine relative location (Fig. 9).
Fig. 9.
As an animal (in experiments, typically a rodent) explores a space, place cells fire in discrete locations that are mapped onto the space as place fields (b, colored ovals)
(A) Shown are seven place cells firing, with some temporal overlap. (B) Top: The seven corresponding place fields, along with a fragment of an animal's trajectory (dashed line). Bottom: The elements of the nerve (a.k.a. Čech) simplicial complex generated by the overlaps among place fields. To form a simplicial complex, each place field center is considered to be a vertex, and each link between vertices is a simplex.
Each simplex σij or σijk is labeled to indicate the vertices linked, e.g., σ617 indicates a link between vertices 6,1 and 7. (C) Persistent homology “barcodes” show the timelines of 0D and 1D loops, respectively: each colored horizontal line represents one 0D loop (top panel) or one 1D loop (bottom panel). The time Tmin
(dotted red vertical lines) marks the moment when spurious loops (topological ‘noise’) disappear and the correct number of loops persists, in this case one in 0D and one in 1D, indicating that there is one hole in the environment. Thus, Tmin is the time after which the correct topological information emerges, which corresponds to the map formation or learning time in this environment, for this particular ensemble of place cells, operating under particular conditions of mean firing rate, mean place field size, number of cells in the population [9]
Place cells in the hippocampus have been determined to show two main functions: sensing the animal’s location relative to significant nearby landmarks, and measuring the distance and direction traveled from a previous location during travel [10]. The latter function is what is referred to as “path integration”.
During travel, position and direction change frequently, so there is a need for a mechanism to periodically check location and direction within place fields, and this is done through phase precession which relies on theta sequences (theta waves firing in bursts with a set frequency that are in the
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background of other brain functions) that can quantify progression in a time-compressed manner [1].
Phase precession is when place cells fire in increasingly earlier phases of the theta cycle as they move in a certain direction (Fig.10).
Fig.10.
Phase precession of hippocampal place cells.
A, Place cells were recorded in rats while they were running back and forth on a linear track. B, Top-view spatial firing map of a cell for the entire session. C, Hippocampal EEG and firing activity of the same cell (red bars) during a single firing field crossing. As the rat crosses the field, the cell bursting activity occurs at a slightly higher frequency than the theta EGG signal. On successive cycles, APs appear in advance (precess) to the peak of the theta wave. Long vertical bars represent the 0° theta phase (i.e., the trough of
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the theta wave). D, the theta phase of APs from the same cell is plotted against position, for multiple field crossings. Each AP is plotted twice, by a factor of 360°; r, linear–circular correlation coefficient [11]
In phase precession, the place cell firing is related to theta oscillations in the hippocampus where multiple overlapping place fields exhibit differing firing intervals in different phases of the theta sequence depending on how close they are [11]. In other words, place fields the centers of which are close (more overlapping) have a short time interval and those which are far have long ones. This phenomenon of the interaction between overlapping place fields in terms of temporal firing is called temporal compression (Fig. 11). Temporal compression can be used here as a form of code that enables the brain to evaluate motion in terms of increasing overlap of place fields (place fields that were once distant become closer, and the decreasing interval can be used to determine how close they have and are becoming). Finally, this code can be used to potentiate long term potentiation (LTP) which is the cornerstone of Hebbian plasticity in memory formation at the synaptic level [11].
Fig.11.
Schematic representation of the compression of temporal sequences phenomenon
Place cells with partially overlapping place fields (red and blue) will show a different firing relationship depending on whether the field centers are distant (left) or close to each other (right). There is a linear relationship between the inter-field distance (D) and the time required to go from one field center to another (running time, T). Because each cell firing is doing phase precession, there is also a relationship between the running time (T) and the time interval between action potentials within the same theta cycle (t). Cells with close fields will tend to fire at a short time interval, whereas cells with distant fields will fire
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at long time intervals. Black traces, EEG during the crossing of both place fields. Red and blue vertical lines, APs of each cell [11]
Each place cell has multiple place fields each depending on the size of the environment assayed.
An interesting finding to note is that place cells in the dentate gyrus (DG), CA1, and CA3 each function slightly differently: CA3 place fields act in concert with entorhinal grid cells linear combination patterns, DG place cells may be divided into two distinct populations that function in parallel, and CA1 place cells are more active in open environments more than the others (indicating an effect from the shape of the environment on place cell mapping). From this, it can be concluded that each ensemble of place cell excitation is unique and this is how the estimation of location occurs (Fig. 12, 13, 14).
Fig. 12.
A histological section illustrating the recording location as well as five simultaneously-recorded place cells is given for each example
The number below each firing rate map is the lowest rate in the red color category [12]
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Place field A) number; B) size; and C) spatial organization, D) proportion of active pixels [12]
22 Fig. 14.
Place field A) coherence; B) location-independent rate; C) information content; and D) rate change ratio (change in firing across environments) [12]
Theta precession has multiple functions relating to the hippocampus’ ability to learn how to navigate environments: it enlarges the analyzed region and makes the analysis occur at a more rapid and reliable pace (Fig.15); it also resolved spurious loops quicker making the entire ensemble “smarter”
(Appendix B) [9].
23 Fig. 15.
Theta precession accelerates learning time
(A) Point clouds representing the mean learning times Tmin computed for the θ-off case (left) and the maps are driven by a θ–signal recorded in the rat (right). Each point corresponds to a place cell ensemble with a specific number of place cells, N, the mean ensemble firing rate, f, the mean ensemble place field size s.
Dark blue circles represent those ensembles that form correct topological maps most rapidly and reliably;
as the color shades from blue through green, yellow, and red, the learning times increase and map formation becomes less reliable (see [4], Methods). The rat θ–signal enlarges the learning region L and speeds map formation. (B) To zero in on the effect of θ precession on the quality of learning, these clouds depict only the maps that converged at least 7 out of 10 times (ρ ≥0.7), and for which the variance of the learning times, ξ = ΔTmin/Tmin, did not exceed 30% of the mean value. Even in this more rigorously defined core of L, with ensembles that already function well, the θ–signal has a pronounced effect [9]
Phase precession seemed to imply a form of predictive element to place cell function which was further explained by [13]. In this article, the distinction was drawn between apparent place fields and true place fields (where true fields are a fraction of the size of apparent ones) distinguished by position in the field. Furthermore, it was shown that since a location ahead of the current one fires simultaneously with the theta sequence for the current location, this process may have a predictive element for future movement.
24 6.3 Grid Cell Function and Distribution
Hafting et al. (2005) was a culmination of several studies to determine the source of the upstream signal for place cells; it was found to be in the mEC and generated by a cell type that fired in a grid-like pattern termed grid cells. Grid cells were then found to be stellate cells mostly located in layer II of the mEC which comprises approximately one in every four of these cells in the mEC [14]. These cells were also nontopographically distributed (grid cell proximity to another grid cell did not mean their firing patterns were adjacent). Grid cells are divided functionally into nodules that are consistent internally but not in comparison to each other while it remains unknown how these modules are distributed and structured (Fig. 16). It is this uniformity within and variety without that can account for the proportional change in velocity of movement and neural sheet displacement. This is consistent with the finding that each grid module can function independently due to the demonstration of independent and varying responses to environmental stimuli [15].
25 Fig.16.
Grid cell organization and function in navigation (A) An animal navigates past three locations in a square environment.
(B) Firing fields of three grid cells with different phases (a, b, and c) active at the three locations in (A) (1, 2, and 3, respectively) and their relative phase offsets, shown in the phase overlay.
(C) Two possible anatomical organizations of grid cells under the synaptic connectivity predicted by CAN models. (i) Grid cell somas are organized by their phase offsets. Top: at every location the animal passes, the cell sheet exhibits multiple activity bumps arranged in a triangular lattice. The cells generating these bumps have identical phases in the environment (e.g., all cells at magenta bumps have identical phases as cell a in (B)). The lattice of activity bumps moves along with the animal’s trajectory so that the trajectory in the environment is mapped to multiple activity trajectories on the cell sheet. Middle: cells with identical phases are anatomically arranged at vertices of a triangular lattice. Bottom: relative anatomical arrangement of cells in local brain neighborhoods corresponds to their phase offsets (phase overlay in (B)) and repeats on the cell sheet. (ii) Grid cell somas and activity bumps are randomly arranged. The anatomical arrangement of cells with identical phases (middle) and the relative arrangements of cells in local neighborhoods (bottom) are random [14]
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There is also a variation in grid cell pattern size which progressively increases when moving from the dorsal to the ventral aspects of the medial entorhinal cortex (Fig. 17) which can be explained by theta phase precession in the entorhinal cortex modulating grid cell firing based on the stable baseline frequency formed by the rhythmic oscillation of the full cell population [16].
Fig. 17.
Theta phase precession using the persistent spiking neuron model with 5 Hz baseline frequency A. The dorsal entorhinal cortex is simulated with parameter P(z)=0.0193, resulting in a larger shift in persistent firing frequency (and faster shift in phase) for a given velocity. Black dots at the top show phase of spiking versus location during multiple passes through firing fields. Line 1 (green) shows the summed activity of all three populations of persistent spiking neurons, resulting in grid cell spiking in Line 4. Line 2 shows the baseline persistent spiking frequency. Line 3 depicts the EEG oscillation that might be expected from rhythmic spiking (with a downward deflection during strongest excitatory input). Note that the grid cell spiking (Line 4) shifts in phase relative to the peak of the EEG oscillation shown by vertical gray lines. B. Simulation of a more ventral entorhinal neuron using a smaller value for parameter P(z)=0.0048. This results in a smaller shift in persistent spiking frequency (and slower shift in phase) for a given velocity and results in a larger grid field and a slower shift in firing phase relative to background theta, consistent with experimental data [16]
The only flaw in this argument is the as-yet lack of corroboration of theta oscillations in all layers of the mEC that contain grid cells which may require further study.
As for grid cell firing itself, grid cell maps show stability in spatial location but not firing rates with variation in external variables (shape and color of the enclosure) in the same environment (Fig. 18) [17]. This is very similar to what is recorded in place cells during partial remapping which is another
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interesting parallel between these cell types that distinguishes them from the other surrounding cell populations in this specific firing context.
Fig. 18.
Grid cell stability in spatial location with variation in firing rates
(A) Schematic of the experimental paradigms. (B) Firing-rate maps of an example grid cell that was recorded across all conditions. The peak rate of the cell across each set of four sessions is indicated to the right. (C) To measure the spatial stability between sessions, spatial cross-correlations were computed for the rate maps of each grid cell. For the example cell in (B), spatial cross-correlation maps are shown
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across matching conditions (left column) and different shapes (right, top), colors (right, middle), and locations (right, bottom). Cross-correlation matrices are scaled from the minimum to the maximum correlation coefficient (noted to the right, blue to red). The displacement of the central peak from the origin measures the extent of the shift in the grid pattern. (D) Displacement from the origin when manipulating box shape, color, or location. For each comparison, the shift between repetitions of matching conditions was subtracted from the shift between sessions in different conditions (Δ displacement). Grid cells exhibited a significant displacement for shape, color, and location [17]
Grid cells divide the environment into triangular or hexagonal grid fields that fire whenever the animal passes over specific regions that form this grid [1]. The model for grid cell activation has been a matter of debate with two primary theories: the single-cell spiking model and the continuous attractor network model. The single-cell spiking model relies on external factors to affect neurons that either stimulate or inhibit grid cells that then form patterns of activation through Hebbian plasticity (in other words, through repeated and persistent stimulation inducing the synapse to learn a specific pattern of excitation) [18]. The continuous attractor network model is one in which grid cells “intrinsic properties drive activity towards a stable state”; the stable state is achieved at the firing location which is determined through path integration [19] with some independence from external stimuli when it comes to pattern formation. [20] though established that visual factors affect grid cell firing (Fig. 20), so the single-cell spiking model may be more accurate even though both have their flaws as are highlighted in the abovementioned papers referenced. On the other hand, [16] points out that the visual inputs mentioned above can be used alongside other inputs from head direction cells in the postsubiculum or deep layers of the medial entorhinal cortex to shift the phase of a population of persistent firing cells that perpetuate grid cell firing only when they are in phase with each other which only occurs when the organism is facing one of the vertices of the grid cell pattern (Fig. 19).
29 Fig. 19.
Mechanism for interaction of persistent firing cells to cause grid cell firing
A. Spiking activity over time of three different groups of persistent firing neurons. Here, each group consists of three persistent spiking cells firing with a baseline frequency of 3 Hz with different phases.
Cells receive input from head direction (HD) cells with a 0-degree preferred angle for Group 1, 120- degree angle for Group 2, and 240-degree angle for Group 3. Grid cell firing arises from the convergent spiking of the three groups of persistent firing neurons. When all three persistent firing groups fire in synchrony, the grid cell will fire (red dots). B. Persistent firing cells with 4 Hz baseline frequency in a two-dimensional circular environment. Dots indicate the location of the virtual rat during each spike, showing no spatial specificity. In contrast, the phase of spiking depends on location. Dot color (light to dark blue) indicates a phase of spike relative to a single reference oscillation. C. Grid cell spiking (red
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dots) occurs only when all of the persistent firing neurons fire at the same phase, resulting in a typical grid cell firing pattern. The gray line indicates the rat’s trajectory from experimental data [16]
Fig. 20.
The effects of external inputs on grid cell firing
A. Conjunctive grid cell with spiking determined by persistent spiking cells combined with input from a single head direction cell. B. The head direction specificity of the conjunctive cell spiking shown in red demonstrates the strong directional selectivity of this conjunctive cell. Blue dashed line shows total dwell time at different head directions. C. Grid cell generated with persistent firing input without additional direct head-direction input. D. The red line shows a lack of head direction specificity for grid cells generated with persistent spiking cells input alone. E. Simulation of grid cell firing based on phase determined by the angle and distance of 3 visual stimuli. F. Grid cell simulation with a phase determined by visual stimulus viewed from two eyes with a 120-degree difference in visual angle [16]
Additionally, connections between stellate cells in layer II of the mEC are almost exclusively inhibitory, but coupled with an external source of excitatory activity are capable of forming a network that
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can perform the function of grid cells [15]. The external source of excitatory potentials can be the hippocampus through the connections of the EC stellate cells to the DG and CA3 regions, and it has been shown that hippocampal lysis eliminates the vast majority of grid cell function (Fig. 21) [15]. The main flaw in this model is the lack of noise tolerance but that can be ameliorated through synaptic facilitation (very transient enhancement of synaptic transmission) and gain modulation (combining the information from two or more sources into one signal) which are demonstrated to occur in the mEC [15].
Fig. 21.
Inhibition-based attractor network model for grid cells
(a) A hexagonal grid pattern forms spontaneously (here over a period of 500 ms) on a two-dimensional neuronal lattice consisting of stellate cells that have all-or-none inhibitory connections with each other.
Neurons are arranged on the lattice according to their spatial phases. Activity is color-coded, as indicated by the scale bar at the bottom. Connection radii R of two example neurons are shown as white and green circles (diameter 2R). Note lower activity where the circles overlap (at 500 ms). (b) Simulated single-neuron activity (red dots) over 10 min of foraging in a 1.8 m diameter circular arena. W0 is the strength of the inhibitory connectivity of the network; R is the radius. Note that W0 and R control the size of the grid fields and their spacing. (c) Effect of excitatory drive from the hippocampus. Spike distribution plots (as in b) and directional tuning curves (firing rate as a function of direction) for two example cells in the presence of strong hippocampal output (top) and weak hippocampal output (bottom). (d) Grid scores
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(sixfold rotational symmetry) and mean vector length (directional tuning) as a function of the strength of external input (means ± s.e.m.). With large hippocampal inputs, high grid scores are obtained, as in the top image in (c). When the external input is decreased below a critical amount, as in the bottom image of (c), the activity on the neuronal sheet gets easily distorted and hexagonal structure is not detectable over time. At the same time, the head-directional input becomes the dominant source of input and the neurons show high directional tuning. (a,b) [15]
Most of the previous experiments were performed on mice, but there are variations in development between species especially when it comes to the brain, and this case is no different: in mice, a mature innervation pattern appears around day 10 postnatal, approximately 2 weeks in macaques (and one would assume around as much in other monkeys), but it takes more than 22 weeks for the rudiments of these patterns (with mature structure) to appear in humans [21].
6.4 Place and Grid Cell Interaction
To understand how these cell types interact, it is appropriate to begin with how the hippocampus and entorhinal cortex are connected. They are generally considered to be arranged in a circuit beginning in the entorhinal cortex projecting into the dentate gyrus, followed by the CA3 Sommer region, then region CA3, region CA2, the subiculum, and finally back to the entorhinal cortex [3] (Fig. 22).
Fig. 22.
33 A schematic showing a dorsal view of the hippocampus with the dentate gyrus (DG) visible inside The main hippocampus body as well as the uncus are indicated. b-e| Coronal slices showing the hippocampal subfields in the anterior hippocampus. Red lines indicate subfields that are found within the uncus and which have distinct cytoarchitectural properties relative to the main body of the hippocampus.
The slices in parts b and c are at the level of the uncinate gyrus, whereas slice d is at the level of the intralimbic gyrus. The arrows in part e indicate the flow of information in the canonical hippocampal circuit in the body of the hippocampus. Pro, prosubiculum; PrS/PaS, presubiculum ,and parasubiculum;
EC, entorhinal cortex; HATA=hippocampal-amygdaloid transition area [3]
Developmentally, place cells predate grid cells in appearance by about a week in mice and generally predate them in other organisms. As a result, it can be assumed that place cells can generate place fields independent of grid cell function which is what was found by [22] where new place fields were generated despite theta oscillation disruption and thus grid cell disruption, as well as these new fields persisting after recovery from inactivation. What grid cells may do in terms of place cell function is clarified in [23] where increasing place field accuracy away from the borders of an enclosure was shown to coincide with the development of grid cell patterns in post-natal days 21-22 that are equivalent to those found in adults (other potential explanations may justify further, more precise experimentation though the correlation is itself fascinating). This would imply that grid cell patterns are used by the hippocampus to refine existing place fields to increase accuracy and sensitivity at around the time the animal may increasing need it as it normally begins to navigate the world around it with increasing regularity.
Both of the cell types mentioned above are connected and act in concert as was mentioned in [2]
among others. This interaction takes many forms. [24] showed that grid cell depolarization (but not hyperpolarization) can cause place cell remapping even though they both caused changes in firing rates implying the presence of another structure that contextualizes these external stimuli.
Furthermore, [25] showed that the increased size of grid cell fields increases place field size especially farther from border regions and that increased grid field size can decrease the stability of place fields associated with them, so it can be concluded that grid cells have a role in refining place fields and their mapping (further illustrated in [26]). But, [26] also showed that grid cells are not necessary for place field remapping even though they do have a demonstrable effect in that the change is not the same in these cases.
34 6.5 Place and Grid Cell Role in Memory Formation
Memory is the organism’s ability to encode, store, and retrieve information when needed. Each type of memory has its discrete system(s) that potentiate this function. Episodic memory, as it relates to spatial perception, is dealt with in the medial temporal lobe. The prevailing theory is that this process is done through long-term potentiation and/or depression in a Hebbian pattern [27].
Place and grid cells have different roles in memory encoding, consolidation, and retrieval. The process of place map memory formation in the hippocampus is illustrated in [2] where selective attention encoded in synchronous oscillatory activity that is a result of learned association favors memory encoding.
This is followed by weak potentiation followed by increasing ripples that consolidate the memory in the affected synapses. Retrieval is done through replay which they also document. These are episodic memories and it is well known that these memories are stored in the hippocampus.
35 Fig. 23.
Activation of different areas of the hippocampus depending on the action done
a-i| The images show the results of a selection of fMRI studies in which the anteromedial hippocampus (amHipp) (arrows) was engaged by imagination and recall (a-f) and visual perception (g-i) of scenes and events. Colored regions are those in which there was an increased hemodynamic response during each task (in part g, colors represent % of subjects (n=34) with activation, range 50-80%). The tasks involved constructing static atemporal scenes(a), constructing and elaborating upon imagined events(b, the white circle indicates the hippocampus), recalling past events and imagining events set in the past and future(c), imagining specific rather than general future events(d), autobiographical memory retrieval(e and f), viewing novel scenes relative to scrambled images(g), viewing scenes in which an object had been moved relative to the background(h, the white circle indicates hippocampus) or correct versus incorrect scene oddity judgments(i). j| The overlap in activity between the perception and imagination of scenes.
Hippocampal activation for scene perception relative to object perception is shown in red, activity for imagining scenes versus imagining objects is shown in blue and the overlap is shown in turquoise (also shown in sagittal and coronal slices) [3]
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This figure (Fig. 23) illustrates the essential role of the medial aspect of the anterior hippocampus in many aspects and types of memory with involvement in processes related to recall such as construction (done mainly in the medial anterior hippocampus) and elaboration (done mainly in the posterior hippocampus which is also where visual stimuli are processed [3]. Construction in memory is inferring past events from stored memory while elaboration in memory is associating meaningful information with non-meaningful information to make the latter easier to remember.
Fig. 24.
MECIII input to hippocampal CA1 is crucial for trace fear conditioning
(A) Diagram of entorhinal hippocampal circuits. Stellate Ocean cells (purple) in the ECII project to the DG, CA3, and CA2 regions, whereas ECIII cells (orange) directly project to the CA1 region. (B) Possible mechanisms. Tone-induced MECIII persistent activity may activate the CA1 pyramidal cells to bridge the temporal gap during the trace period of memory task [29]
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As previously mentioned, there are two main circuits between the entorhinal cortex and the hippocampus: the trisynaptic circuit from MEC II to the dentate gyrus then CA3 then CA1 then back to layer V of the mEC, and there is a direct circuit from MEC III and CA1 (Fig. 25).
Fig.25
Island cells gate the MECIII input into the CA1 pyramidal cells through the feedforward inhibition (A) New diagram of entorhinal hippocampal circuits. Pyramidal Island cells (blue, ECIIi) directly project to stratum lacunosum (SL) of the CA1 region to synapse with the GABAergic interneurons (green) in SL (SL-INs). (B) Strategic location of SL-INs and projections from MECIII and Island cells. Island cell axons innervate the stratum lacunosum (SL), whereas MECIII cells axons innervate the stratum moleculare (SM) immediately adjacent to the SL. This strategic location of SL-INs (green), the primary target of Island cells, immediately adjacent to the inner side of the SM layer where MECIII cells synapse to the distal dendrites of CA1 pyramidal cells (blue) enables Island cells to suppress MECIII input (purple) by feedforward inhibition [29]
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The first has a significant part to play in the formation of spatial memory while the second plays a role in adding temporal context to spatial memories; in other words, putting episodic memories in order of time of occurrence which is done by a certain cell type called time cells that separate distinct events from each other in order of occurrence (Fig. 26) while also creating ensembles of associated events which was disrupted due to septal inactivation. Furthermore, both circuits, linked through both synapsing with CA1 pyramidal cells at different locations have been shown to interact (the trisynaptic circuit causing feedforward inhibition of the direct circuit) in the control of temporal associative memory (whether or not this feedforward inhibition is engaged may determine memory and event association) [29].
Fig. 26.
Time-cell firing sequences during the trace period of a memory task
Cartoon of a raster display of spiking activity recorded time cells. Each cell is shown in a different color.
For each cell, activity is shown as a raster of spikes for three example trials in which the cell fires for a brief period at approximately the same moment in each trial [29]
Relating to the CA1 region of the hippocampus, it has been previously noted that there is a series of repeating ripples consisting of reactivation of previously stimulated place cell ensemble reactivation
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during subsequent slow-wave sleep for 50-100 ms for one to three times depending on the distance traveled. This ripple was preceded and eventually synchronized with another such ripple in the mEC during quiet-awake sleep that is related to it since inhibition of the initial mEC ripple reduces the subsequent CA1 ripple. The arrangement of these ripples is the first ripple in the mEC followed by one in the CA1 region with an interval of 25-125 ms with the subsequent interval between the ripples from both sources decreasing until they overlap. It is believed that the quiet-awake (on-line) ripples in the mEC initiate ripples in the CA1 region used for planning and navigation while the CA1 region initiates ripples in the slow-wave state (offline) to the higher cortical region for memory consolidation [30].
Interestingly, similar ripples have been discovered in awake states relating to certain activities and navigational tasks where this replay is important seemingly for both the aforementioned functions.
Also, navigational data in place sequences are played here in reverse order of occurrence seemingly to emphasize the most recent event, though which events are replayed when depends on what is needed to perform an intended task. As a result, this phenomenon posits a mechanism through which memory consolidation outside of the hippocampus occurs explaining how some memories (spatial or otherwise) survive hippocampal damage [31].
As for grid cells, [1] stated that they shift coherently in grid cell modules from environment to environment. Also, they posited that theta sequences contained a portion that is significant in mind-travel (what they called phase precession). Since place and grid cells are physically and physiologically associated, it stands to reason that the environmental memories formed are a combination of the two, as well as other associated cell types.
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7. METHODOLOGY AND METHODS
Selection criteria were applied to ensure a thorough review of the literature. These sources included peer-reviewed journals, medical databases, and scientific databases. The selected sources were published between the years 2008 and 2019.
To collect literature, the main terms of the study were put in the search engine of the selected database and the relevant results were selected as references (in this paper, 30 in total). An example of said search was putting “place cell function” in Pubmed with a year interval of 2008 to 2019 and sifting through the results for relevant, highly cited papers that would then guide further research into the specific topic in question.
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8. RESULTS AND DISCUSSION
From the literature, it becomes clear that there are many unknown elements to this system and what parts each of its constituents play. The research discussed here repeatedly mentions different unanswered questions and attempts to answer them from various angles, with issues in execution that shall soon be discussed below.
Place cells are agreed to be pyramidal cells located in the CA1, CA3, and DG regions of the hippocampus and act in spatial mapping and determining location and movement direction. The questions begin to arise as to how they perform these functions: either with each place cell as an independent actor that can activate the system independently or as a part of an ensemble of place cells that codes for a specific region but only as a whole can the entire process be initiated. Here, the results begin to diverge with each researcher providing an argument for or against each of them, or, in some instances, showing that at different levels of specificity, you might see one or the other. Further experimentation into how place cells relate to each other in terms of stimulation and potentiation of spatial function is necessary and may now be possible with more specific techniques that can narrow down the scope of an analyzed brain region immensely.
Another question is the plasticity of place cells; how many place fields does each one have: some say one, some say more depending on the cell assayed and the species of animal which also brings us to the question of place field size: is it uniform among all place cells of a specific region? Or do they vary?
And if they do why? [8] discussed a flaw that was found frequently in previous (and can be noticed in some subsequent) experiments when it comes to the size of place fields which may falsely give the sense of the presence of only one place field per place cell when there can instead be multiple as they demonstrated; this opens the questions as to where the cutoff point for place cells to have one or more fields is and if it varies from one region of the brain to another, and how place cells choose which place field to utilize when; combine that with the need to reproduce the results of this experiment consistently to remove all doubt or potential for error and you find a significant question about the basic organization of place cells to be answered.
Furthermore, the lack of research into how place cells differentiate in the first place is a quite glaring opening in the existing research, but how different neurons in the brain differentiate into different cell populations remains vague. Many researchers cite many differences in distribution and some functional characteristics between different species without a detailed description as to what they are and
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how they might skew results when attempting to extrapolate the behavior of human variants of these cells based on animal results.
Place cells in different regions of the hippocampus have slight differences in function that are not yet explained even though they have been experimentally observed leaving the question of the effects of place cells’ surrounding cellular environment on them and how this effect is enacted.
As for grid cells, they are generally presumed to be stellate cells located almost exclusively in the mEC that help refine place fields and specify and streamline spatial perception by dividing the surrounding area into triangular/hexagonal grids where each vertex is a focus of firing. Questions begin to arise as to the model underlying their firing with two prevailing ones: the single-cell spiking and continuous attractor model. Both have their proponents with a variety of arguments from observations made in experiments but neither has fully taken hold in the field as a whole which may imply that the true mechanism may instead be a hybrid of both but further, more pinpoint experimentation must be done to collect the necessary data to conclusively determine the reality of the situation.
Also, grid cells are divided into modules that function in concert to denote a specific environment, but the distribution and arrangement of these modules is unknown. Moreover, models for grid cell functional regulation show an as yet undefined role for place cells that requires further study to elucidate in full. Finally, grid cells mature at different times across species and the reason why remains unclear.
The interaction between place cells and grid cells is for refining as complete a spatial image of the organism’s surroundings as possible before committing it to memory for later use. The question here may relate to the effect of one cell type on the maturation of the other; place cells have been shown to appear first in the brains of assayed animals followed by grid cells, but the effect of place cells on grid cell maturation and generation remains unclear. Another point is if place cells have any upstream regulation; if so how and by what structure? (it is attempts at answering this question that led to the discovery of grid cells while this question remains yet to be answered). Eventually, this leads to the question of the extent of the connection between these two cell types and if it is completely reciprocal or more one-sided as some functional results may posit (lack of grid cell firing did not inhibit place cell remapping ([26], but the converse may not be true).
Finally, when it comes to memory formation, there are many questions to be answered. In the beginning, memory formation is the process by which data is encoded, consolidated, and stored for future use. What signal starts the memory formation process is not specified (or if it is spontaneous), which factor is more important: recency or importance when it comes to what is and isn’t recalled and the importance of context, but more importantly what specific circuit is used to denote that factor and express
