Place Cells Research Articles

Visualization of the functional neural circuit breakdown process in a mouse model of Alzheimer's disease and application to the development of new therapies

Alzheimer's disease (AD) patients cause cognitive impairment in time and space as early symptoms, in which the hippocampus plays an essential role. In the hippocampus, place cells play an important role in processing spatial information. The hippocampus recognizes space and guides creatures to their destinations using these place cells. In AD patients, the hippocampal neuronal network that place cells form could malfunction with age. However, it has remained unknown how the disruption of the neural circuits progresses at a single-cell resolution in AD. Conventional methods such as electrophysiology experiments could only record the same cells in the hippocampal neural circuit of awake mice for a few days. To clear the detail of neuronal circuit disruption in AD, it is necessary to observe brains of the same individuals over several months. Therefore, we performed chronic two-photon calcium imaging using AD model mice expressing the fluorescent calcium sensor protein G-CaMP7 to monitor spatiotemporal representation in a virtual reality environment. The activity of hundreds of hippocampal CA1 pyramidal neurons was detected over months from the same neuronal populations. In AD mice, amyloid (Aβ) plaque-like aggregates in the stratum oriens layer of hippocampal CA1 start to develop at 2.5 months of age, increase in both number and size with age, and neurons with hyperactivity increase near Aβ plaque-like aggregates. In addition, place cells observed during VR navigation showed abnormalities in the stability of activity within the place field at 4 months old, and then the stability was further deteriorated and the number of place cells decreased at 7 months old. Thus, we demonstrated that the activity patterns of various neurons, including place cells, in the neural circuitry of the hippocampal CA1 region in AD have different failure modes, and this finding is expected to be applied to the effective drug evaluation for AD.

Full-electric microfluidic platform to capture, analyze and selectively release single cells.

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

Persistent representation of the environment in the hippocampus

In the hippocampus, environmental changes elicit rearrangement of active neuronal ensembles or remapping of place cells. However, it remains elusive how the brain ensures a consistent representation of a certain environment itself despite salient events occurring there. Here, we longitudinally tracked calcium dynamics of dorsal hippocampal CA1 neurons in mice subjected to contextual fear conditioning and extinction training. Overall population activities were significantly changed by fear conditioning and were responsive to footshocks and freezing. However, a small subset of neurons, termed environment cells, were consistently active in a specific environment irrespective of experiences. A decoder modeling study showed that these cells, but not place cells, were able to predict the environment to which the mouse was exposed. Environment cells might underlie the constancy of cognition for distinct environments across time and events. Additionally, our study highlights the functional heterogeneity of cells in the hippocampus.

A proposed attention-based model for spatial memory formation and retrieval.

Animals use sensory information and memory to build internal representations of space. It has been shown that such representations extend beyond the geometry of an environment and also encode rich sensory experiences usually referred to as context. In mammals, contextual inputs from sensory cortices appear to be converging on the hippocampus as a key area for spatial representations and memory. How metric and external sensory inputs (e.g., visual context) are combined into a coherent and stable place representation is not fully understood. Here, I review the evidence of attentional effects along the ventral visual pathway and in the medial temporal lobe and propose an attention-based model for the integration of visual context in spatial representations. I further suggest that attention-based retrieval of spatial memories supports a feedback mechanism that allows consolidation of old memories and new sensory experiences related to the same place, thereby contributing to the stability of spatial representations. The resulting model has the potential to generate new hypotheses to explain complex responses of spatial cells such as place cells in the hippocampus.

Influences of the present crises from Romania on employment

The article is showing the main consequances of the multiple but especially energy crises on some of the relevant social and economic indicators of Romania during 2020-2022, with accent on the field of employment and working places. We analize also, the intervention of the Romanian social protection system in order to improve quality of life for the poor population, the used data being compared with European relevant context. The article is supporting its allegations by some objective indicators published by Eurostat, Romanian National Institute for Statistics, UniCredit Romania, Bruegel.org and others.

The expanded circuitry of hippocampal ripples and replay

The place cells and well-defined oscillatory population rhythms of the rodent hippocampus have served as a powerful model system in linking cells and circuits to memory function. While the initial three decades of place cell research primarily focused on the activity of neurons during exploration, the last twenty-five years have seen growing interest in the physiology of the hippocampus at rest. During slow-wave sleep and quiet wakefulness the hippocampus exhibits sharp-wave ripples (SWRs), short high-frequency, high-amplitude oscillations, that organize the reactivation or ‘replay’ of sequences of place cells, and interventions that disrupt SWRs impair learning. While the canonical model of SWRs generation have emphasized CA3 input to CA1 as the source of excitatory drive, recent work suggests there are multiple circuits, including the CA2 region, that can both influence, generate and organize SWRs, both from the oscillatory and information content perspectives in a task and state-dependent manner. This extended circuitry and its function must be considered for a true understanding of the role of the hippocampus in off-line processes such as planning and consolidation.

Experience-dependent changes in hippocampal spatial activity and hippocampal circuit function are disrupted in a rat model of Fragile X Syndrome

BackgroundFragile X syndrome (FXS) is a common single gene cause of intellectual disability and autism spectrum disorder. Cognitive inflexibility is one of the hallmarks of FXS with affected individuals showing extreme difficulty adapting to novel or complex situations. To explore the neural correlates of this cognitive inflexibility, we used a rat model of FXS (Fmr1−/y).MethodsWe recorded from the CA1 in Fmr1−/y and WT littermates over six 10-min exploration sessions in a novel environment—three sessions per day (ITI 10 min). Our recordings yielded 288 and 246 putative pyramidal cells from 7 WT and 7 Fmr1−/y rats, respectively.ResultsOn the first day of exploration of a novel environment, the firing rate and spatial tuning of CA1 pyramidal neurons was similar between wild-type (WT) and Fmr1−/y rats. However, while CA1 pyramidal neurons from WT rats showed experience-dependent changes in firing and spatial tuning between the first and second day of exposure to the environment, these changes were decreased or absent in CA1 neurons of Fmr1−/y rats. These findings were consistent with increased excitability of Fmr1−/y CA1 neurons in ex vivo hippocampal slices, which correlated with reduced synaptic inputs from the medial entorhinal cortex. Lastly, activity patterns of CA1 pyramidal neurons were dis-coordinated with respect to hippocampal oscillatory activity in Fmr1−/y rats.LimitationsIt is still unclear how the observed circuit function abnormalities give rise to behavioural deficits in Fmr1−/y rats. Future experiments will focus on this connection as well as the contribution of other neuronal cell types in the hippocampal circuit pathophysiology associated with the loss of FMRP. It would also be interesting to see if hippocampal circuit deficits converge with those seen in other rodent models of intellectual disability.ConclusionsIn conclusion, we found that hippocampal place cells from Fmr1−/y rats show similar spatial firing properties as those from WT rats but do not show the same experience-dependent increase in spatial specificity or the experience-dependent changes in network coordination. Our findings offer support to a network-level origin of cognitive deficits in FXS.

Place cells are nonrandomly clustered by field location in CA1 hippocampus.

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

Symmetries and asymmetries in the neural encoding of 3D space.

The neural coding of space centres on three foundational cell types: place cells, head direction cells and grid cells. One notable characteristic of these neurons is the symmetry properties of their spatial firing patterns. In symmetric environments, firing patterns are often also symmetric: for example, place cells show translational symmetry in aligned sub-compartments of a multi-compartment environment. A single head direction cell has a mirror-symmetric firing pattern, while a sub-class of head direction cells can show multi-fold rotational symmetries in multi-compartment environments, matching the symmetry of the recently experienced environment. The entorhinal grid cells are notable for the symmetry of their firing patterns in both rotational and translational domains. However, these symmetries are broken in a variety of situations. These symmetry-making and -breaking observations shed light on the underlying computations that generate these firing patterns, and also invite speculation as to whether they may have a functional role. This article outlines these findings and speculates on the consequences of the resultant firing symmetries and asymmetries for spatial coding and cognition. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.

Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning.

Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth analysis of how to distribute place cell fields according to the obstacles in cluttered environments to optimize learning time and path optimality during goal-oriented spatial navigation tasks. The analysis uses a reinforcement learning (RL) model that assumes that place cells allow encoding the state. While previous studies have suggested exploiting different field sizes to represent areas requiring different spatial resolutions, our work analyzes specific distributions that adapt the representation to the environment, activating larger fields in open areas and smaller fields near goals and subgoals (e.g., obstacle corners). In addition to assessing how the multi-scale representation may be exploited in spatial navigation tasks, our analysis and results suggest place cell representations that can impact the robotics field by reducing the total number of cells for path planning without compromising the quality of the paths learned.

Compositional Sequence Generation in the Entorhinal-Hippocampal System.

Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas such as the prefrontal cortex, indicating that it may be a general principle of neural functionality in the brain. In a recent analysis through the lens of dynamical systems theory, we showed how grid coding can lead to the generation of a diversity of empirically observed sequential reactivations of hippocampal place cells corresponding to traversals of cognitive maps. Here, we extend this sequence generation model by describing how the synthesis of multiple dynamical systems can support compositional cognitive computations. To empirically validate the model, we simulate two experiments demonstrating compositionality in space or in time during sequence generation. Finally, we describe several neural network architectures supporting various types of compositionality based on grid coding and highlight connections to recent work in machine learning leveraging analogous techniques.

Significance of visual scene-based learning in the hippocampal systems across mammalian species.

The hippocampus and its associated cortical regions in the medial temporal lobe play essential roles when animals form a cognitive map and use it to achieve their goals. As the nature of map-making involves sampling different local views of the environment and putting them together in a spatially cohesive way, visual scenes are essential ingredients in the formative process of cognitive maps. Visual scenes also serve as important cues during information retrieval from the cognitive map. Research in humans has shown that there are regions in the brain that selectively process scenes and that the hippocampus is involved in scene-based memory tasks. The neurophysiological correlates of scene-based information processing in the hippocampus have been reported as "spatial view cells" in nonhuman primates. Like primates, it is widely accepted that rodents also use visual scenes in their background for spatial navigation and other kinds of problems. However, in rodents, it is not until recently that researchers examined the neural correlates of the hippocampus from the perspective of visual scene-based information processing. With the advent of virtual reality (VR) systems, it has been demonstrated that place cells in the hippocampus exhibit remarkably similar firing correlates in the VR environment compared with that of the real-world environment. Despite some limitations, the new trend of studying hippocampal functions in a visually controlled environment has the potential to allow investigation of the input-output relationships of network functions and experimental testing of traditional computational predictions more rigorously by providing well-defined visual stimuli. As scenes are essential for navigation and episodic memory in humans, further investigation of the rodents' hippocampal systems in scene-based tasks will provide a critical functional link across different mammalian species.

Subtle visual change in a virtual environment induces heterogeneous remapping systematically in CA1, but not CA3

Environmental change may lead to new memories or modify old ones, but the underlying neural mechanisms are largely unclear. We recorded hippocampal place cells simultaneously from CA1 and CA3 in a virtual reality environment. Compared with CA1, place cells in CA3 are more tolerant of individual landmark changes but undergo orthogonal changes to code distinctively different environments. As visual noise (virtual fog) is introduced to a visually enriched environment, place cells in CA1 split into two subpopulations: in one, place cells maintain their field locations while changing their firing rates to reflect sensory changes; in the other, place cells exhibit global remapping in response to the contextual change. In contrast, place cells in CA3 exhibit mainly rate remapping under the same conditions. Our results suggest that CA1 may simultaneously represent heterogeneous maps of the same environment when subtle visual noise induces both sensory and contextual changes.

Neuromorphic model of hippocampus place cells using an oscillatory interference technique for hardware implementation

In this paper, we propose a simplified and robust model for place cell generation based on the oscillatory interference (OI) model concept. Aiming toward hardware implementation in bio-inspired simultaneous localization and mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent theta cells, and allows the theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the theta cell’s base oscillation frequency and gain—as a function of modulatory inputs—is demonstrated. Place cell composed of 48 theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM. Hence, we propose a model for spatial cell formation using theta cells with behaviors that are biologically plausible and hardware implementable for real world application in neurally-inspired SLAM.

Robotic Navigation Based on Experiences and Predictive Map Inspired by Spatial Cognition

Humans and animals have environmental cognition and navigation abilities. These abilities are closely related to the spatial cognitive mechanism of brain. Based on this mechanism, we propose a novel robotic navigation framework based on experiences and predictive map inspired by spatial cognition to accurately construct environment experience and quickly plan path. The grid cell and the place cell are modeled to rapidly integrate self-motion cues. The multidimensional grid coding and one-shot learning rule are utilized for activating the place representation of robot pose. Visual cues provide information for relocation. These information are integrated through experiences which express the topology of the environment, enabling the robot to accurately achieve spatial cognition of complex environment. In order to realize the sequential decision making of hippocampus, the predictive map is introduced to quickly plan the experience sequence to target in dynamic environment. The successor representation model of robot's state is constructed through reinforcement learning. Combined with the goal-based reward function, the shortest path can be replanned to adapt to environment changes. The proposed method is tested in simulated maze, Kitti dataset and corridor environment. Compared with other bionic navigation methods, it has a faster computing speed and higher precision with bionic characteristics. Compared with visual navigation methods, the navigation tasks can robustly be accomplished without complex system design.

Accommodating representation in the neuroscience of memory: a conceptual blending analysis of replay and preplay in hippocampal place cell research

Abstract Neurons called place cells are selectively activated in correspondence with the location or place field that a rodent occupies. In a phenomenon that neuroscientists call replay, place cell activation sequences rapidly repeat during subsequent periods of rest and grooming. Replay has been theorized as a mechanism for reinforcement learning of the spatial trajectories represented by place cell coactivation. Preplay is a competing theory that suggests that these sequences also occur before a novel run and that sequences are not recordings of position made in real time, but rather pre-made repertoires that an organism selects from as it makes a trajectory through space. The preplay theory maintains the language of representation while breaking from the entailment of the conceptual metaphor “MEMORIES ARE RECORDINGS” that recordings are produced simultaneously to the experiences that they represent. It does so through a conceptual blend that affords preplay researchers flexibility in their theorizing about memory without requiring a break from representationalism. Broadly, these findings demonstrate how the blending of conceptual metaphors is a viable approach for the implicit development and contestation of theories of representation in the neural and cognitive sciences.

A Virtual Reality Platform for Context-Dependent Cognitive Research in Rodents.

Animal survival necessitates adaptive behaviors in volatile environmental contexts. Virtual reality (VR) technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental context by simulating the real world with maximized control of contextual elements. Yet current VR tools for rodents have limited flexibility and performance (e.g., frame rate) for context-dependent cognitive research. Here, we describe a high-performance VR platform with which to study contextual behaviors immersed in editable virtual contexts. This platform was assembled from modular hardware and custom-written software with flexibility and upgradability. Using this platform, we trained mice to perform context-dependent cognitive tasks with rules ranging from discrimination to delayed-sample-to-match while recording from thousands of hippocampal place cells. By precise manipulations of context elements, we found that the context recognition was intact with partial context elements, but impaired by exchanges of context elements. Collectively, our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.

Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal

Hippocampal place cells support reward-related spatial memories by forming a cognitive map that over-represents reward locations. The strength of these memories is modulated by the extent of reward expectation during encoding. However, the circuit mechanisms underlying this modulation are unclear. Here we find that when reward expectation is extinguished in mice, they remain engaged with their environment, yet place cell over-representation of rewards vanishes, place field remapping throughout the environment increases, and place field trial-to-trial reliability decreases. Interestingly, Ventral Tegmental Area (VTA) dopaminergic axons in CA1 exhibit a ramping reward-proximity signal that depends on reward expectation and inhibiting VTA dopaminergic neurons largely replicates the effects of extinguishing reward expectation. We conclude that changing reward expectation restructures CA1 cognitive maps and determines map reliability by modulating the dopaminergic VTA-CA1 reward-proximity signal. Thus, internal states of high reward expectation enhance encoding of spatial memories by reinforcing hippocampal cognitive maps associated with reward.

Entorhinal cortex directs learning-related changes in CA1 representations

Learning-related changes in brain activity are thought to underlie adaptive behaviours1,2. For instance, the learning of a reward site by rodents requires the development of an over-representation of that location in the hippocampus3–6. How this learning-related change occurs remains unknown. Here we recorded hippocampal CA1 population activity as mice learned a reward location on a linear treadmill. Physiological and pharmacological evidence suggests that the adaptive over-representation required behavioural timescale synaptic plasticity (BTSP)7. BTSP is known to be driven by dendritic voltage signals that we proposed were initiated by input from entorhinal cortex layer 3 (EC3). Accordingly, the CA1 over-representation was largely removed by optogenetic inhibition of EC3 activity. Recordings from EC3 neurons revealed an activity pattern that could provide an instructive signal directing BTSP to generate the over-representation. Consistent with this function, our observations show that exposure to a second environment possessing a prominent reward-predictive cue resulted in both EC3 activity and CA1 place field density that were more elevated at the cue than at the reward. These data indicate that learning-related changes in the hippocampus are produced by synaptic plasticity directed by an instructive signal from the EC3 that seems to be specifically adapted to the behaviourally relevant features of the environment.