Place Cell Activity Research Articles

Mechanisms of memory-supporting neuronal dynamics in hippocampal area CA3.

Hippocampal CA3 is central to memory formation and retrieval. Although various network mechanisms have been proposed, direct evidence is lacking. Using intracellular Vm recordings and optogenetic manipulations in behaving mice, we found that CA3 place-field activity is produced by a symmetric form of behavioral timescale synaptic plasticity (BTSP) at recurrent synapses among CA3 pyramidal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not the DG was required to update place cell activity based on the animal's movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Theoretical analyses further highlight the superior memory storage capacity of such networks, especially when dealing with correlated input patterns. This evidence elucidates the cellular and circuit mechanisms of learning and memory formation in the hippocampus.

Investigating Egocentric Tuning in Hippocampal CA1 Neurons.

Navigation requires integrating sensory information with a stable schema to create a dynamic map of an animal's position using egocentric and allocentric coordinate systems. In the hippocampus, place cells encode allocentric space, but their firing rates may also exhibit directional tuning within egocentric or allocentric reference frames. We compared experimental and simulated data to assess the prevalence of tuning to egocentric bearing (EB) among hippocampal cells in rats foraging in an open field. Using established procedures, we confirmed egocentric modulation of place cell activity in recorded data; however, simulated data revealed a high false-positive rate (FPR). When we accounted for false positives by comparing with shuffled data that retain correlations between the animal's direction and position, only a very low number of hippocampal neurons appeared modulated by EB. Our study highlights biases affecting FPRs and provides insights into the challenges of identifying egocentric modulation in hippocampal neurons.

Barcoding of episodic memories in the hippocampus of a food-caching bird

The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees-food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.

Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity.

Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.

Integrated Three-Electrode Dual-Mode Detection Chip for Place Cell Analysis: Dopamine Facilitates the Role of Place Cells in Encoding Spatial Locations of Novel Environments and Rewards.

The functioning of place cells requires the involvement of multiple neurotransmitters, with dopamine playing a critical role in hippocampal place cell activity. However, the exact mechanisms through which dopamine influences place cell activity remain largely unknown. Herein, we present the development of the integrated three-electrode dual-mode detection chip (ITDDC), which enables simultaneous recording of the place cell activity and dopamine concentration fluctuation. The working electrode, reference electrode, and counter electrode are all integrated within the ITDDC in electrochemical detection, enabling the real-time in situ monitoring of dopamine concentrations in animals in motion. The reference, working, and counter electrodes are surface-modified using PtNPs and polypyrrole, PtNPs and PEDOT:PSS, and PtNPs, respectively. This modification allows for the detection of dopamine concentrations as low as 20 nM. We conducted dual-mode testing on mice in a novel environment and an environment with food rewards. We found distinct dopamine concentration variations along different paths within a novel environment, implying that different dopamine levels may contribute to spatial memory. Moreover, environmental food rewards elevate dopamine significantly, followed by the intense firing of reward place cells, suggesting a crucial role of dopamine in facilitating the encoding of reward-associated locations in animals. The real-time and in situ recording capabilities of ITDDC offer new opportunities to investigate the interplay between electrophysiology and dopamine during animal exploration and reward-based memory and provide a novel glimpse into the correlation between dopamine levels and place cell activity.

Making Room for the Place-Grid Coupled Dynamics

Under the “spatial modulation continuous attractor network” (SCAN) model, insightful theoretical discussions have been proposed arguing that no direct sensory input signaling velocity to grid cells is required during locomotion, and no place cell activity seems to constitute a summation of grid cells. The model focuses less on explaining the dynamic relationship between place and grid cells as a fundamental component for coding space and time. This commentary highlights, however, that the place-grid coupled dynamics could represent the core mechanism that keeps coherent spatial coded information. This coupling mode between grid- and place-cells seems weak in SCAN, but it is irreducible and crucial for cognitive mapping.

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Grid cells and place cells represent the spatiotemporal continuum of an animal's past, present, and future locations. However, their spatiotemporal relationship is unclear. Here, we co-record grid and place cells in freely foraging rats. We show that average time shifts in grid cells tend to be prospective and are proportional to their spatial scale, providing a nearly instantaneous readout of a spectrum of progressively increasing time horizons ranging hundreds of milliseconds. Average time shifts of place cells are generally larger compared to grid cells and also increase with place field sizes. Moreover, time horizons display nonlinear modulation by the animal's trajectories in relation to the local boundaries and locomotion cues. Finally, long and short time horizons occur at different parts of the theta cycle, which may facilitate their readout. Together, these findings suggest that population activity of grid and place cells may represent local trajectories essential for goal-directed navigation and planning.

No evidence that acute clozapine administration alters CA1 phase precession in rats

Hippocampal phase precession, wherein there is a systematic shift in the phase of neural firing against the underlying theta activity, is proposed to play an important role in the sequencing of information in memory. Previous research shows that the starting phase of precession is more variable in rats following maternal immune activation (MIA), a known risk factor for schizophrenia. Since starting phase variability has the potential to disorganize the construction of sequences of information, we tested whether the atypical antipsychotic clozapine, which ameliorates some cognitive deficits in schizophrenia, alters this aspect of phase precession. Either saline or clozapine (5 mg/kg) was administered to rats and then CA1 place cell activity was recorded from the CA1 region of the hippocampus as the animals ran around a rectangular track for food reward. When compared to saline trials, acute administration of clozapine did not affect any place cell properties, including those related to phase precession, in either control or MIA animals. Clozapine did, however, produce a reduction in locomotion speed, indicating that its presence had some effect on behaviour. These results help to constrain explanations of phase precession mechanisms and their potential role in sequence learning deficits.

Differential behavior-related activity of distinct hippocampal interneuron types during odor-associated spatial navigation

Hippocampal pyramidal cells represent an animal's position in space together with specific contexts and events. However, it is largely unknown how distinct types of GABAergic interneurons contribute to such computations. We recorded from the intermediate CA1 hippocampus of head-fixed mice exhibiting odor-to-place memory associations during navigation in a virtual reality (VR). The presence of an odor cue and its prediction of a different reward location induced a remapping of place cell activity in the virtual maze. Based on this, we performed extracellular recording and juxtacellular labeling of identified interneurons during task performance. The activity of parvalbumin (PV)-expressing basket, but not of PV-expressing bistratified cells, reflected the expected contextual change in the working-memory-related sections of the maze. Some interneurons, including identified cholecystokinin-expressing cells, decreased activity during visuospatial navigation and increased activity during reward. Our findings suggest that distinct types of GABAergic interneuron are differentially involved in cognitive processes of the hippocampus.

Hippocampal place cells exhibit impairments in spatial information coding in an APP‐knock in model of Alzheimer’s Disease

AbstractBackgroundThe hippocampus is one of the first cortical regions to exhibit Alzheimer’s Disease (AD) pathology. The spatially‐related firing of hippocampal place cells provides the cellular basis for spatial memory and this is impaired relatively early in AD, yet few studies examine place cell activity in AD mouse models. We undertook an in‐depth characterisation of hippocampal pyramidal cell activity in an APP knock‐in model of AD.MethodMicrodrives carrying 8 tetrodes were implanted into the left CA1 hippocampal subfield of 14‐month female mice (four APPNL‐G‐F, four wild‐type littermates). Mice undertook daily foraging sessions in an open field and ran on a linear track as CA1 activity was recorded. On completion of experiments, 40µm coronal sections of fixed brain tissue were stained using Cresyl Violet to verify tetrode placement and Thioflavin‐S to quantify amyloid β plaque burden.Result270 cells were recorded (119 wild‐type, 151 APPNL‐G‐F). CA1 pyramidal cells were classified as “place cells” or “non‐place cells” based on their spatial information content (bits/spike). Pyramidal cells recorded in wild‐type mice had significantly greater spatial information content than APPNL‐G‐F pyramidal cells (Figure 1), and a higher proportion were classified as place cells. Wild‐type place cells had significantly greater spatial information content than APPNL‐G‐F place cells. Corresponding differences were identified in place field size (Figures 2, 3). A positive correlation was identified between Aβ plaque burden and pyramidal cell spatial information content in the four APPNL‐G‐F mice (Figure 4).Coordination of place cell firing by the hippocampal theta rhythm was disrupted in APPNL‐G‐F mice; phase‐locking of pyramidal cells and place cells was significantly reduced (Figure 5), there was less coherence in the preferred theta phase across the pyramidal cell population, and a reduction in theta phase precession was observed.ConclusionAPPNL‐G‐F CA1 place cells exhibited deficits in both rate coding and temporal coding of spatial information indicating that amyloid β pathology, in the absence tau tangles, was associated with a disruption of hippocampal place cell function. These results provide preliminary support for the hypothesis that disruption of hippocampal function by AD pathology manifests as altered place cell activity and spatial behaviour.

The temporal and contextual stability of activity levels in hippocampal CA1 cells

Recent long-term optical imaging studies have demonstrated that the activity levels of hippocampal neurons in a familiar environment change on a daily to weekly basis. However, it is unclear whether there is any time-invariant property in the cells' neural representations. In this study, using miniature fluorescence microscopy, we measured the neural activity of the mouse hippocampus in four different environments every 3d. Although the activity level of hippocampal neurons fluctuated greatly in each environment across days, we found a significant correlation between the activity levels for different days, and the correlation was higher for averaged activity levels across multiple environments. When the number of environments used for averaging was increased, a higher activity correlation was observed. Furthermore, the number of environments in which a cell showed activity was preserved. Cells that showed place cell activity in many environments had greater spatial information content and more stable spatial representation, and thus carried more abundant and stable information about the current position. In contrast, cells that were active only in a small number of environments provided sparse representation for the environment. These results suggest that each cell has not only an inherent activity level but also play a characteristic role in the coding of space.

Synergies between synaptic and HCN channel plasticity dictates firing rate homeostasis and mutual information transfer in hippocampal model neuron.

Homeostasis is a precondition for any physiological system of any living organism. Nonetheless, models of learning and memory that are based on processes of synaptic plasticity are unstable by nature according to Hebbian rules, and it is not fully clear how homeostasis is maintained during these processes. This is where theoretical and computational frameworks can help in gaining a deeper understanding of the various cellular processes that enable homeostasis in the face of plasticity. A previous simplistic single compartmental model with a single synapse showed that maintaining input/output response homeostasis and stable synaptic learning could be enabled by introducing a linear relationship between synaptic plasticity and HCN conductance plasticity. In this study, we aimed to examine whether this approach could be extended to a more morphologically realistic model that entails multiple synapses and gradients of various VGICs. In doing so, we found that a linear relationship between synaptic plasticity and HCN conductance plasticity was able to maintain input/output response homeostasis in our morphologically realistic model, where the slope of the linear relationship was dependent on baseline HCN conductance and synaptic permeability values. An increase in either baseline HCN conductance or synaptic permeability value led to a decrease in the slope of the linear relationship. We further show that in striking contrast to the single compartment model, here linear relationship was insufficient in maintaining stable synaptic learning despite maintaining input/output response homeostasis. Additionally, we showed that homeostasis of input/output response profiles was at the expense of decreasing the mutual information transfer due to the increase in noise entropy, which could not be fully rescued by optimizing the linear relationship between synaptic and HCN conductance plasticity. Finally, we generated a place cell model based on theta oscillations and show that synaptic plasticity disrupts place cell activity. Whereas synaptic plasticity accompanied by HCN conductance plasticity through linear relationship maintains the stability of place cell activity. Our study establishes potential differences between a single compartmental model and a morphologically realistic model.

Exercise accelerates place cell representational drift

Stable neural ensembles are often thought to underlie stable learned behaviors and memory. Recent longitudinal experiments, however, that tracked the activity of the same neurons over days to weeks have shown that neuronal activity patterns can change over extended timescales even if behaviors remain the same - a phenomenon termed representational drift1. We have tested whether neural circuit remodeling, defined as any change in structural connectivity, contributes to representational drift. To do this, we tracked how hippocampal CA1 spatial representations of a familiar environment change with time in conventionally housed mice relative to mice housed with a running wheel. Voluntary exercise is an environmental stimulus that promotes hippocampal circuit remodeling, primarily via promoting adult neurogenesis in the dentate gyrus. Adult neurogenesis alters structural connectivity patterns, as the integration of adult-generated granule cells (abGCs) is a competitive process where new input-output synaptic connections may co-exist and/or even replace existing synaptic connections2. Comparing the spatial activity of downstream hippocampal CA1 place cells in the same familiar environment over two weeks, we found that the activity of place cells in exercise mice exhibited accelerated representational drift compared to control mice, suggesting that hippocampal circuit remodeling may indeed drive representational drift.

The chicken and egg problem of grid cells and place cells.

Place cells and grid cells are major building blocks of the hippocampal cognitive map. The prominent forward model postulates that grid-cell modules are generated by a continuous attractor network; that a velocity signal evoked during locomotion moves entorhinal activity bumps; and that place-cell activity constitutes summation of entorhinal grid-cell modules. Experimental data support the first postulate, but not the latter two. Several families of solutions that depart from these postulates have been put forward. We suggest a modified model (spatial modulation continuous attractor network; SCAN), whereby place cells are generated from spatially selective nongrid cells. Locomotion causes these cells to move the hippocampal activity bump, leading to movement of the entorhinal manifolds. Such inversion accords with the shift of hippocampal thought from navigation to more abstract functions.

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

A robotic model of hippocampal reverse replay for reinforcement learning.

Hippocampal reverse replay, a phenomenon in which recently active hippocampal cells reactivate in the reverse order, is thought to contribute to learning, particularly reinforcement learning (RL), in animals. Here, we present a novel computational model which exploits reverse replay to improve stability and performance on a homing task. The model takes inspiration from the hippocampal-striatal network, and learning occurs via a three-factor RL rule. To augment this model with hippocampal reverse replay, we derived a policy gradient learning rule that associates place-cell activity with responses in cells representing actions and a supervised learning rule of the same form, interpreting the replay activity as a 'target' frequency. We evaluated the model using a simulated robot spatial navigation task inspired by the Morris water maze. Results suggest that reverse replay can improve performance stability over multiple trials. Our model exploits reverse reply as an additional source for propagating information about desirable synaptic changes, reducing the requirements for long-time scales in eligibility traces combined with low learning rates. We conclude that reverse replay can positively contribute to RL, although less stable learning is possible in its absence. Analogously, we postulate that reverse replay may enhance RL in the mammalian hippocampal-striatal system rather than provide its core mechanism.

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.

Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats

Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.

Gateway identity and spatial remapping in a combined grid and place cell attractor

The spatial specificities of hippocampal place cells, i.e., their firing fields, are subject to change if the rat enters a new compartment in the experimental maze. This effect is known as remapping. It cannot be explained from path integration (grid cell activity) and local sensory cues alone but requires additional knowledge of the different compartments in the form of context recognition at the gateways between them. Here we present a model for the hippocampal–entorhinal interplay in which the activity of place and grid cells follows a joint attractor dynamic. Place cells depend on the current grid cell activity but can also reset the grid cell activity in the remapping process. Remapping is triggered by the passage through a gateway. When this happens, a previously stored pattern of place cell activity associated with the gateway is reactivated from a “gateway database”. The joint attractor will then reinstate the grid cell pattern that was active when the gateway had first been learned and path integration can proceed from there. The model is tested with various mazes used in the experimental literature and reproduces the published results, and we make predictions for remapping in a new maze type. We propose the involvement of memory in the form of “gate cells” that drive the place cells and with them the joint hippocampal–entorhinal loop into the corresponding attractor whenever a compartment is entered.

Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex.

Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given behavioral significance and how cortical networks might encode this information. We first demonstrate that rats can associate different speed patterns on the same trajectory with distinct behavioral choices. In this novel experimental paradigm, rats follow a small baited robot in a large megaspace environment where the rat's speed is precisely controlled by the robot's speed. Based on this proof of concept and research showing that recurrent reservoir networks are ideal for representing spatio-temporal structures, we then test reservoir networks in simulated navigation contexts and demonstrate they can discriminate between traversals of the same path with identical durations but different speed profiles. We then test the networks in an embodied robotic setup, where we use place cell representations from physically navigating robots as input and again successfully discriminate between traversals. To demonstrate that this capability is inherent to recurrent networks, we compared the model against simple linear integrators. Interestingly, although the linear integrators could also perform the speed profile discrimination, a clear difference emerged when examining information coding in both models. Reservoir neurons displayed a form of statistical mixed selectivity as a complex interaction between spatial location and speed that was not as abundant in the linear integrators. This mixed selectivity is characteristic of cortex and reservoirs and allows us to generate specific predictions about the neural activity that will be recorded in rat cortex in future experiments.