Uncovering spatial topology represented by rat hippocampal population neuronal codes

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.     

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales,attention and oscillations

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model''s parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.     

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity

 A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot. Received: 02 July 1999 / Accepted in revised form: 20 March 2000     

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.     

Increased attention to spatial context increases both place field stability and spatial memory

The hippocampal formation is critical for the acquisition and consolidation of memories. When recorded in freely moving animals, hippocampal pyramidal neurons fire in a location-specific manner: they are "place" cells, comprising a hippocampal representation of the animal's environment. To explore the relationship between place cells and spatial memory, we recorded from mice in several behavioral contexts. We found that long-term stability of place cell firing fields correlates with the degree of attentional demands and that successful spatial task performance was associated with stable place fields. Furthermore, conditions that maximize place field stability greatly increase orientation to novel cues. This suggests that storage and retrieval of place cells is modulated by a top-down cognitive process resembling attention and that place cells are neural correlates of spatial memory. We propose a model whereby attention provides the requisite neuromodulatation to switch short-term homosynaptic plasticity to long-term heterosynaptic plasticity, and we implicate dopamine in this process.     

Representing episodes in the mammalian brain

Memory lets the past inform the present so that we can attain future goals. In many species, these abilities require the hippocampus. Recent experiments, in which memory demand was varied while overt behavior and the environment were kept constant, have revealed firing patterns of hippocampal neurons that corresponded with memory demands and predicted performance. Although the active population appeared to be 'place cells' that signalled location, it actually included cells the activity patterns of which distinguished the recent or pending history of behavior during identical actions that occurred in the same place. Different populations of hippocampal cells fired as a rat walked along the same spatial path on the way to different goals, and coded past, present and pending events. Other experiments provide converging data that neuronal activity is modulated by goal-directed behavioral episodes. Together, these firing patterns suggest a testable mechanism of episodic memory coding: that hippocampal dynamics encode a temporally extended, hierarchically organized representation of goal-directed behavior.     

Animat navigation using a cognitive graph

 This article describes a computational model of the hippocampus that makes it possible for a simulated rat to navigate in a continuous environment containing obstacles. This model views the hippocampus as a “cognitive graph”, that is, a hetero-associative network that learns temporal sequences of visited places and stores a topological representation of the environment. Calling upon place cells, head direction cells, and “goal cells”, it suggests a biologically plausible way of exploiting such a spatial representation for navigation that does not require complicated graph-search algorithms. Moreover, it permits “latent learning” during exploration, that is, the building of a spatial representation without the need of any reinforcement. When the rat occasionally discovers some rewarding place it may wish to rejoin subsequently, it simply records within its cognitive graph, through a series of goal and sub-goal cells, the direction in which to move from any given start place. Accordingly, the model implements a simple “place-recognition-triggered response” navigation strategy. Two implementations of place cell management are studied in parallel. The first one associates place cells with place fields that are given a priori and that are uniformly distributed in the environment. The second one dynamically recruits place cells as exploration proceeds and adjusts the density of such cells to the local complexity of the environment. Both implementations lead to identical results. The article ends with a few predictions about results to be expected in experiments involving simultaneous recordings of multiple cells in the rat hippocampus. Received: 25 June 1999 / Accepted in revised form: 20 March 2000     

Neural basis for spatial memory in animals: what do hippocampal neurons tell us?

Recent studies relying on the recording of neuronal unit activity in freely moving rats show the existence of two populations of neurons signalling the animal's location or head direction: place cells found primarily in the hippocampus and head direction cells found in brain areas anatomically and functionally related to the hippocampus. The properties of these two neuronal populations suggest that their activity strongly depends upon information cues stemming from the spatial environment, and also suggest their involvement in spatial memory. Place cells and head direction cells would jointly participate in a neural network allowing the animal to orient in space and to store spatial locations in memory. This network would also be operating in humans, in particular for encoding specific events in episodic memory.     

Ensemble place codes in hippocampus: CA1, CA3, and dentate gyrus place cells have multiple place fields in large environments

Previously we reported that the hippocampus place code must be an ensemble code because place cells in the CA1 region of hippocampus have multiple place fields in a more natural, larger-than-standard enclosure with stairs that permitted movements in 3-D. Here, we further investigated the nature of hippocampal place codes by characterizing the spatial firing properties of place cells in the CA1, CA3, and dentate gyrus (DG) hippocampal subdivisions as rats foraged in a standard 76-cm cylinder as well as a larger-than-standard box (1.8 m×1.4 m) that did not have stairs or any internal structure to permit movements in 3-D. The rats were trained to forage continuously for 1 hour using computer-controlled food delivery. We confirmed that most place cells have single place fields in the standard cylinder and that the positional firing pattern remapped between the cylinder and the large enclosure. Importantly, place cells in the CA1, CA3 and DG areas all characteristically had multiple place fields that were irregularly spaced, as we had reported previously for CA1. We conclude that multiple place fields are a fundamental characteristic of hippocampal place cells that simplifies to a single field in sufficiently small spaces. An ensemble place code is compatible with these observations, which contradict any dedicated coding scheme.     

Role of EGR1 in hippocampal synaptic enhancement induced by tetanic stimulation and amputation

Hippocampal neurons fire spikes when an animal is at a particular location or performs certain behaviors in a particular place, providing a cellular basis for hippocampal involvement in spatial learning and memory. In a natural environment, spatial memory is often associated with potentially dangerous sensory experiences such as noxious or painful stimuli. The central sites for such pain-associated memory or plasticity have not been identified. Here we present evidence that excitatory glutamatergic synapses within the CA1 region of the hippocampus may play a role in storing pain-related information. Peripheral noxious stimulation induced excitatory postsynaptic potentials (EPSPs) in CA1 pyramidal cells in anesthetized animals. Tissue/nerve injury caused a rapid increase in the level of the immediate-early gene product Egr1 (also called NGFI-A, Krox24, or zif/268) in hippocampal CA1 neurons. In parallel, synaptic potentiation induced by a single tetanic stimulation (100 Hz for 1 s) was enhanced after the injury. This enhancement of synaptic potentiation was absent in mice lacking Egr1. Our data suggest that Egr1 may act as an important regulator of pain-related synaptic plasticity within the hippocampus.     

Conjunctive spatial and self-motion codes are topographically organized in the GABAergic cells of the lateral septum

The hippocampal spatial code’s relevance for downstream neuronal populations—particularly its major subcortical output the lateral septum (LS)—is still poorly understood. Here, using calcium imaging combined with unbiased analytical methods, we functionally characterized and compared the spatial tuning of LS GABAergic cells to those of dorsal CA3 and CA1 cells. We identified a significant number of LS cells that are modulated by place, speed, acceleration, and direction, as well as conjunctions of these properties, directly comparable to hippocampal CA1 and CA3 spatially modulated cells. Interestingly, Bayesian decoding of position based on LS spatial cells reflected the animal’s location as accurately as decoding using the activity of hippocampal pyramidal cells. A portion of LS cells showed stable spatial codes over the course of multiple days, potentially reflecting long-term episodic memory. The distributions of cells exhibiting these properties formed gradients along the anterior–posterior and dorsal–ventral axes of the LS, directly reflecting the topographical organization of hippocampal inputs to the LS. Finally, we show using transsynaptic tracing that LS neurons receiving CA3 and CA1 excitatory input send projections to the hypothalamus and medial septum, regions that are not targeted directly by principal cells of the dorsal hippocampus. Together, our findings demonstrate that the LS accurately and robustly represents spatial, directional as well as self-motion information and is uniquely positioned to relay this information from the hippocampus to its downstream regions, thus occupying a key position within a distributed spatial memory network.

Calcium imaging of neurons in freely behaving mice reveals how the lateral septum, the main output of the hippocampal place cells, effectively represents information about not only location, but also head direction and self-movement, and may be pivotal in sending this information to downstream brain regions.     

Has the hippocampus really forgotten about space?

Several lines of evidence, including the discovery of place cells, have contributed to the notion that the hippocampus serves primarily to navigate the environment, as a repository of spatial memories, like a drawer full of charts; and that in some species it has exapted on this original one an episodic memory function. We argue that recent evidence questions the primacy of space, and points at memory load, whether spatial or not, as the challenge that mammalian hippocampal circuitry has evolved to meet.     

Place Cells,Grid Cells,and Memory

The hippocampal system is critical for storage and retrieval of declarative memories, including memories for locations and events that take place at those locations. Spatial memories place high demands on capacity. Memories must be distinct to be recalled without interference and encoding must be fast. Recent studies have indicated that hippocampal networks allow for fast storage of large quantities of uncorrelated spatial information. The aim of the this article is to review and discuss some of this work, taking as a starting point the discovery of multiple functionally specialized cell types of the hippocampal–entorhinal circuit, such as place, grid, and border cells. We will show that grid cells provide the hippocampus with a metric, as well as a putative mechanism for decorrelation of representations, that the formation of environment-specific place maps depends on mechanisms for long-term plasticity in the hippocampus, and that long-term spatiotemporal memory storage may depend on offline consolidation processes related to sharp-wave ripple activity in the hippocampus. The multitude of representations generated through interactions between a variety of functionally specialized cell types in the entorhinal–hippocampal circuit may be at the heart of the mechanism for declarative memory formation.The scientific study of human memory started with Herman Ebbinghaus, who initiated the quantitative investigation of associative memory processes as they take place (Ebbinghaus 1885). Ebbinghaus described the conditions that influence memory formation and he determined several basic principles of encoding and recall, such as the law of frequency and the effect of time on forgetting. With Ebbinghaus, higher mental functions were brought to the laboratory. In parallel with the human learning tradition that Ebbinghaus started, a new generation of experimental psychologists described the laws of associative learning in animals. With behaviorists like Pavlov, Watson, Hull, Skinner, and Tolman, a rigorous program for identifying the laws of animal learning was initiated. By the middle of the 20th century, a language for associative learning processes had been developed, and many of the fundamental relationships between environment and behavior had been described. What was completely missing, though, was an understanding of the neural activity underlying the formation of the memory. The behaviorists had deliberately shied away from physiological explanations because of the intangible nature of neural activity at that time.Then the climate began to change. Karl Lashley had shown that lesions in the cerebral cortex had predictable effects on behavior in animals (Lashley 1929, 1950), and Donald Hebb introduced concepts and ideas to account for complex brain functions at the neural circuit level, many of which have retained a place in modern neuroscience (Hebb 1949). Both Lashley and Hebb searched for the engram, but they found no specific locus for it. A significant turning point was reached when Scoville and Milner (1957) reported severe loss of memory in an epileptic patient, patient H.M., after bilateral surgical removal of the hippocampal formation and the surrounding medial temporal lobe areas. “After operation this young man could no longer recognize the hospital staff nor find his way to the bathroom, and he seemed to recall nothing of the day-to-day events of his hospital life.” This tragic misfortune inspired decades of research on the function of the hippocampus in memory. H.M.’s memory impairment could be reproduced in memory tasks in animals and studies of H.M., as well as laboratory animals, pointed to a critical role for the hippocampus in declarative memory—memory, which, in humans, can be consciously recalled and declared, such as memories of experiences and facts (Milner et al. 1968; Mishkin 1978; Cohen and Squire 1980; Squire 1992; Corkin 2002). What was missing from these early studies, however, was a way to address the neuronal mechanisms that led information to be stored as memory.The aim of this article is to show how studies of hippocampal neuronal activity during the past few decades have brought us to a point at which a mechanistic basis of memory formation is beginning to surface. An early landmark in this series of investigations was the discovery of place cells, cells that fire selectively at one or few locations in the environment. At first, these cells seemed to be part of the animal’s instantaneous representation of location, independent of memory, but gradually, over the course of several decades, it has become clear that place cells express current as well as past and future locations. In many ways, place cells can be used as readouts of the memories that are stored in the hippocampus. More recent work has also shown that place cells are part of a wider network of spatially modulated neurons, including grid, border, and head direction cells, each with distinct roles in the representation of space and spatial memory. In this article, we shall discuss potential mechanisms by which these cell types, particularly place and grid cells, in conjunction with synaptic plasticity, may form the basis of a mammalian system for fast high-capacity declarative memory.     

Attention Enhances the Retrieval and Stability of Visuospatial and Olfactory Representations in the Dorsal Hippocampus

A key question in the analysis of hippocampal memory relates to how attention modulates the encoding and long-term retrieval of spatial and nonspatial representations in this region. To address this question, we recorded from single cells over a period of 5 days in the CA1 region of the dorsal hippocampus while mice acquired one of two goal-oriented tasks. These tasks required the animals to find a hidden food reward by attending to either the visuospatial environment or a particular odor presented in shifting spatial locations. Attention to the visuospatial environment increased the stability of visuospatial representations and phase locking to gamma oscillations—a form of neuronal synchronization thought to underlie the attentional mechanism necessary for processing task-relevant information. Attention to a spatially shifting olfactory cue compromised the stability of place fields and increased the stability of reward-associated odor representations, which were most consistently retrieved during periods of sniffing and digging when animals were restricted to the cup locations. Together, these results suggest that attention selectively modulates the encoding and retrieval of hippocampal representations by enhancing physiological responses to task-relevant information.     

GABAergic contributions to gating, timing, and phase precession of hippocampal neuronal activity during theta oscillations

Successful spatial exploration requires gating, storage, and retrieval of spatial memories in the correct order. The hippocampus is known to play an important role in the temporal organization of spatial information. Temporally ordered spatial memories are encoded and retrieved by the firing rate and phase of hippocampal pyramidal cells and inhibitory interneurons with respect to ongoing network theta oscillations paced by intra- and extrahippocampal areas. Much is known about the anatomical, physiological, and molecular characteristics as well as the connectivity and synaptic properties of various cell types in the hippocampal microcircuits, but how these detailed properties of individual neurons give rise to temporal organization of spatial memories remains unclear. We present a model of the hippocampal CA1 microcircuit based on observed biophysical properties of pyramidal cells and six types of inhibitory interneurons: axo-axonic, basket, bistratistified, neurogliaform, ivy, and oriens lacunosum-moleculare cells. The model simulates a virtual rat running on a linear track. Excitatory transient inputs come from the entorhinal cortex (EC) and the CA3 Schaffer collaterals and impinge on both the pyramidal cells and inhibitory interneurons, whereas inhibitory inputs from the medial septum impinge only on the inhibitory interneurons. Dopamine operates as a gate-keeper modulating the spatial memory flow to the PC distal dendrites in a frequency-dependent manner. A mechanism for spike-timing-dependent plasticity in distal and proximal PC dendrites consisting of three calcium detectors, which responds to the instantaneous calcium level and its time course in the dendrite, is used to model the plasticity effects. The model simulates the timing of firing of different hippocampal cell types relative to theta oscillations, and proposes functional roles for the different classes of the hippocampal and septal inhibitory interneurons in the correct ordering of spatial memories as well as in the generation and maintenance of theta phase precession of pyramidal cells (place cells) in CA1. The model leads to a number of experimentally testable predictions that may lead to a better understanding of the biophysical computations in the hippocampus and medial septum.     

Hippocampal Remapping Is Constrained by Sparseness rather than Capacity

Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space.     

Place cells, spatial maps and the population code for memory

The study of population dynamics in hippocampal place cells has emerged as one of the most powerful tools for understanding the encoding, storage and retrieval of declarative memory. Recent work has laid out the contours of an attractor-based hippocampal population code for memory in recurrent circuits of the hippocampus. The code is based on inputs from a topographically organized, path-integration-dependent spatial map that lies upstream in the medial entorhinal cortex. The recurrent networks of the hippocampal formation enable these spatial inputs to be synthesized with nonspatial event-related information.     

Backward shift of head direction tuning curves of the anterior thalamus: comparison with CA1 place fields

The head direction cell system is composed of multiple regions associated with the hippocampal formation. The dynamics of head direction tuning curves (HDTCs) were compared with those of hippocampal place fields. In both familiar and cue-altered environments, as a rat ran an increasing number of laps on a track, the center of mass (COM) of the HDTC tended to shift backward, similar to shifting observed in place cells. However, important differences existed between these cells in terms of the shift patterns relative to the cue-altered conditions, the proportion of backward versus forward shifts, and the time course of shift resetting. The demonstration of backward COM shifts in head direction cells and place cells suggests that similar plasticity mechanisms (such as temporally asymmetric LTP induction or spike timing-dependent plasticity) may be at work in both brain systems, and these processes may reflect a general mechanism for storing learned sequences of neural activity patterns.     

Increased size and stability of CA1 and CA3 place fields in HCN1 knockout mice

Hippocampal CA1 and CA3 pyramidal neuron place cells encode the spatial location of an animal through localized firing patterns called "place fields." To explore the mechanisms that control place cell firing and their relationship to spatial memory, we studied mice with enhanced spatial memory resulting from forebrain-specific knockout of the HCN1 hyperpolarization-activated cation channel. HCN1 is strongly expressed in CA1 neurons and in entorhinal cortex grid cells, which provide spatial information to the hippocampus. Both CA1 and CA3 place fields were larger but more stable in the knockout mice, with the effect greater in CA1 than CA3. As HCN1 is only weakly expressed in CA3 place cells, their altered activity likely reflects loss of HCN1 in grid cells. The more pronounced changes in CA1 likely reflect the intrinsic contribution of HCN1. The enhanced place field stability may underlie the effect of HCN1 deletion to facilitate spatial learning and memory.     

A topological paradigm for hippocampal spatial map formation using persistent homology

An animal's ability to navigate through space rests on its ability to create a mental map of its environment. The hippocampus is the brain region centrally responsible for such maps, and it has been assumed to encode geometric information (distances, angles). Given, however, that hippocampal output consists of patterns of spiking across many neurons, and downstream regions must be able to translate those patterns into accurate information about an animal's spatial environment, we hypothesized that 1) the temporal pattern of neuronal firing, particularly co-firing, is key to decoding spatial information, and 2) since co-firing implies spatial overlap of place fields, a map encoded by co-firing will be based on connectivity and adjacency, i.e., it will be a topological map. Here we test this topological hypothesis with a simple model of hippocampal activity, varying three parameters (firing rate, place field size, and number of neurons) in computer simulations of rat trajectories in three topologically and geometrically distinct test environments. Using a computational algorithm based on recently developed tools from Persistent Homology theory in the field of algebraic topology, we find that the patterns of neuronal co-firing can, in fact, convey topological information about the environment in a biologically realistic length of time. Furthermore, our simulations reveal a "learning region" that highlights the interplay between the parameters in combining to produce hippocampal states that are more or less adept at map formation. For example, within the learning region a lower number of neurons firing can be compensated by adjustments in firing rate or place field size, but beyond a certain point map formation begins to fail. We propose that this learning region provides a coherent theoretical lens through which to view conditions that impair spatial learning by altering place cell firing rates or spatial specificity.