Place Cells Research Articles

How to teach this paper: ‘Behavioral time scale synaptic plasticity underlies CA1 place fields,’ by Bittner and Milstein et al. (2017)

How to teach this paper: ‘Behavioral time scale synaptic plasticity underlies CA1 place fields,’ by Bittner and Milstein et al. (2017)

A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms.

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective.

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state-space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.

Neural Coding as a Statistical Testing Problem

We take the testing perspective to understand what the minimal discrimination time between two stimuli is for different types of rate coding neurons. Our main goal is to describe the testing abilities of two different encoding systems: place cells and grid cells. In particular, we show, through the notion of adaptation, that a fixed place cell system can have a minimum discrimination time that decreases when the stimuli are further away. This could be a considerable advantage for the place cell system that could complement the grid cell system, which is able to discriminate stimuli that are much closer than place cells.

Estimating neuronal firing density: A quantitative analysis of firing rate map algorithms.

The analysis of neurons that exhibit receptive fields dependent on an organism's spatial location, such as grid, place or boundary cells typically begins by mapping their activity in space using firing rate maps. However, mapping approaches are varied and depend on multiple tuning parameters that are usually chosen qualitatively by the experimenter and thus vary significantly across studies. Small changes in parameters such as these can impact results significantly, yet, to date a quantitative investigation of firing rate maps has not been attempted. Using simulated datasets, we examined how tuning parameters, recording duration and firing field size affect the accuracy of spatial maps generated using the most widely used approaches. For each approach we found a clear subset of parameters which yielded low-error firing rate maps and isolated the parameters yielding 1) the least error possible and 2) the Pareto-optimal parameter set which balanced error, computation time, place field detection accuracy and the extrapolation of missing values. Smoothed bivariate histograms and averaged shifted histograms were consistently associated with the fastest computation times while still providing accurate maps. Adaptive smoothing and binning approaches were found to compensate for low positional sampling the most effectively. Kernel smoothed density estimation also compensated for low sampling well and resulted in accurate maps, but it was also among the slowest methods tested. Overall, the bivariate histogram, coupled with spatial smoothing, is likely the most desirable method in the majority of cases.

Dynamics of space coding by mouse hippocampal CA1 field neurons in a free navigation task in different environments

The development of persistent cognitive adaptations in neurons is a key area of focus in contemporary neuroscience. A significant illustration of such adaptations occurs through spatial specializations in place cells with one or more receptive fields that are sensitive to spatial cues (place fields) [1]. In prior research, we extensively examined the dynamics of place field formation in granular layer neurons of the CA1 field in the hippocampus of mice during arbitrary free navigation in a circular track [2]. As the primary factor for maintaining stability in the spatial specialization of place cells, we have introduced dynamic selectivity. This approach enables us to monitor the latency of each place field’s formation and the selectivity dynamics of place cells to their respective fields throughout one or multiple shooting sessions. However, in one-dimensional settings, like the circular track referenced previously, place fields can demonstrate direction-specificity with regards to animal movement [3]. This factor considerably complicates the analysis for arbitrary movement trajectories of animals. In this study, additional experiments were conducted to capture the neural activity of mice in two-dimensional environments. The experiments were conducted in both a rectangular field with objects and a circular arena with a varying number of obstacles. In this paper, we conduct a comparative analysis to examine the critical parameters governing the development of spatial specializations in a one-dimensional circle track and two-dimensional arenas. These parameters include specialization latency, average dynamic selectivity, initial increase in selectivity, and the proportion of “immediate” place fields that remain stable from the first animal visit. The study discovered that the mean selectivity of the place fields escalated in each session, with higher rates observed in subsequent sessions versus the initial session in a novel setting. Additionally, there was a significant occurrence of “immediate” place fields, representing 11% of all place fields in the rounded arena with obstacles, and 25% of all place fields in the circular track. Additionally, a population analysis of neural activity was performed on the first session in a circular track and a round arena with obstacles. Using Laplace eigenmaps for the dimension reduction of population vectors, the trajectory of animals was reconstructed, and the accuracy of this reconstruction agreed with the average dynamic selectivity for each animal included in the analysis. Thus, dynamic selectivity was verified as a measure of the spatial coding quality of the entire registered population of neurons.

Searching for cognitive specializations of neurons using mutual information framework

Of particular interest for researching the cognitive specializations of neurons is their correlation with environmental variables and animal behavior. Mutual information (MI) is a preferable method for measuring such correlations, as it allows for the assessment of non-linear relationships between variables, detects synchronization, and provides both significance and strength quantification. However, calculating MI for real data is significantly challenging. In this study, we used updated MI calculation techniques to analyze the connection between calcium fluorescence signaling and behavioral variables. Our approach encompasses novel strategies which we compiled into a software program known as INTENS (Information-Theoretic Evaluation of Neuronal Specializations), and it enabled to identify specialized neurons in mice hippocampal calcium activity data while they explored the arena with varying levels of novelty. Numerous methods exist for analyzing the relationship between neuron spikes and behavioral variables, including information-theoretical approaches [1]. Extracting information about the relationship between calcium fluorescent signals and behavior is of particular interest due to the signal’s ability to provide crucial information about subthreshold activations of the neuron. In this study, we use the GCMI Gaussian copula entropy method to calculate mutual information [2]. This method relies on the fact that mutual information between two random variables is independent of their marginal distributions and only depends on the type of copula used (a multidimensional distribution where each marginal distribution is uniform). The actual MI was compared to its corresponding values computed on the time-shifted signals for assessing the statistical significance of the computed information association between the calcium signal and the behavioral variable. Additionally, we devised a technique for gauging the strength of the coupling effect. This involved normalizing the mutual information between the fluorescence signal and the behavior with the entropy value of both variables, previously calculated as random variables. Importantly, the approach outlined earlier is effective for analyzing continuous variables such as calcium signal and animal speed, as well as pairs of continuous and discrete variables such as calcium signal and the presence or absence of grooming. The analysis of calcium signals recorded from the CA1 region of the hippocampus revealed neuronal specializations related to the animal’s external environment, such as place cells, and specializations related to its behavioral activities, including neurons activated during running, rearing, and freezing. Some neurons selectively activated in response to discrete parameters included the animal’s location within the arena (center, walls, and corners) and its speed (rest, slow, and fast). A total of 781 specializations were detected across 472 neurons throughout all four sessions of the experiment. Notably, a single neuron could have several specializations. However, more than half (55%) of the neurons were found to have only one specialization.

Neurophysiology of creativity and machine learning applications for creative process’ stages differentiation through assessment of EEG/VP signals

Of particular interest for researching the cognitive specializations of neurons is their correlation with environmental variables and animal behavior. Mutual information (MI) is a preferable method for measuring such correlations, as it allows for the assessment of non-linear relationships between variables, detects synchronization, and provides both significance and strength quantification. However, calculating MI for real data is significantly challenging. In this study, we used updated MI calculation techniques to analyze the connection between calcium fluorescence signaling and behavioral variables. Our approach encompasses novel strategies which we compiled into a software program known as INTENS (Information-Theoretic Evaluation of Neuronal Specializations), and it enabled to identify specialized neurons in mice hippocampal calcium activity data while they explored the arena with varying levels of novelty. Numerous methods exist for analyzing the relationship between neuron spikes and behavioral variables, including information-theoretical approaches [1]. Extracting information about the relationship between calcium fluorescent signals and behavior is of particular interest due to the signal’s ability to provide crucial information about subthreshold activations of the neuron. In this study, we use the GCMI Gaussian copula entropy method to calculate mutual information [2]. This method relies on the fact that mutual information between two random variables is independent of their marginal distributions and only depends on the type of copula used (a multidimensional distribution where each marginal distribution is uniform). The actual MI was compared to its corresponding values computed on the time-shifted signals for assessing the statistical significance of the computed information association between the calcium signal and the behavioral variable. Additionally, we devised a technique for gauging the strength of the coupling effect. This involved normalizing the mutual information between the fluorescence signal and the behavior with the entropy value of both variables, previously calculated as random variables. Importantly, the approach outlined earlier is effective for analyzing continuous variables such as calcium signal and animal speed, as well as pairs of continuous and discrete variables such as calcium signal and the presence or absence of grooming. The analysis of calcium signals recorded from the CA1 region of the hippocampus revealed neuronal specializations related to the animal’s external environment, such as place cells, and specializations related to its behavioral activities, including neurons activated during running, rearing, and freezing. Some neurons selectively activated in response to discrete parameters included the animal’s location within the arena (center, walls, and corners) and its speed (rest, slow, and fast). A total of 781 specializations were detected across 472 neurons throughout all four sessions of the experiment. Notably, a single neuron could have several specializations. However, more than half (55%) of the neurons were found to have only one specialization.

Investigating navigation strategies in the Morris Water Maze through deep reinforcement learning

Navigation is a complex skill with a long history of research in animals and humans. In this work, we simulate the Morris Water Maze in 2D to train deep reinforcement learning agents. We perform automatic classification of navigation strategies, analyze the distribution of strategies used by artificial agents, and compare them with experimental data to show similar learning dynamics as those seen in humans and rodents. We develop environment-specific auxiliary tasks and examine factors affecting their usefulness. We suggest that the most beneficial tasks are potentially more biologically feasible for real agents to use. Lastly, we explore the development of internal representations in the activations of artificial agent neural networks. These representations resemble place cells and head-direction cells found in mouse brains, and their presence has correlation to the navigation strategies that artificial agents employ.

The role of experience in prioritizing hippocampal replay

During sleep, recent memories are replayed by the hippocampus, leading to their consolidation, with a higher priority given to salient experiences. To examine the role of replay in the selective strengthening of memories, we recorded large ensembles of hippocampal place cells while male rats ran repeated spatial trajectories on two linear tracks, differing in either their familiarity or number of laps run. We observed that during sleep, the rate of replay events for a given track increased proportionally with the number of spatial trajectories run by the animal. In contrast, the rate of sleep replay events decreased if the animal was more familiar with the track. Furthermore, we find that the cumulative number of awake replay events occurring during behavior, influenced by both the novelty and duration of an experience, predicts which memories are prioritized for sleep replay, providing a more parsimonious neural correlate for the selective strengthening of memories.

A realistic computational model for the formation of a Place Cell

Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.

A configural context signal simultaneously but separably drives positioning and orientation of hippocampal place fields.

Effective self-localization requires that the brain can resolve ambiguities in incoming sensory information arising from self-similarities (symmetries) in the environment structure. We investigated how place cells use environmental cues to resolve the ambiguity of a rotationally symmetric environment, by recording from hippocampal CA1 in rats exploring a "2-box." This apparatus comprises two adjacent rectangular compartments, identical but with directionally opposed layouts (cue card at one end and central connecting doorway) and distinguished by their odor contexts (lemon vs. vanilla). Despite the structural and visual rotational symmetry of the boxes, no place cells rotated their place fields. The majority changed their firing fields (remapped) between boxes but some repeated them, maintaining a translational symmetry and thus adopting a relationship to the layout that was conditional on the odor. In general, the place field ensemble maintained a stable relationship to environment orientation as defined by the odors, but sometimes the whole ensemble rotated its firing en bloc, decoupling from the odor context cues. While the individual elements of these observations-odor remapping, place field repetition, ensemble rotation, and decoupling from context-have been reported in isolation, the combination in the one experiment is incompletely explained within current models. We redress this by proposing a model in which odor cues enter into a three-way association with layout cues and head direction, creating a configural context signal that facilitates two separate processes: place field orientation and place field positioning. This configuration can subsequently still function in the absence of one of its components, explaining the ensemble decoupling from odor. We speculate that these interactions occur in retrosplenial cortex, because it has previously been implicated in context processing, and all the relevant signals converge here.

Full field-of-view virtual reality goggles for mice

Visual virtual reality (VR) systems for head-fixed mice offer advantages over real-world studies for investigating the neural circuitry underlying behavior. However, current VR approaches do not fully cover the visual field of view of mice, do not stereoscopically illuminate the binocular zone, and leave the lab frame visible. To overcome these limitations, we developed iMRSIV (Miniature Rodent Stereo Illumination VR)-VR goggles for mice. Our system is compact, separately illuminates each eye for stereo vision, and provides each eye with an ∼180° field of view, thus excluding the lab frame while accommodating saccades. Mice using iMRSIV while navigating engaged in virtual behaviors more quickly than in a current monitor-based system and displayed freezing and fleeing reactions to overhead looming stimulation. Using iMRSIV with two-photon functional imaging, we found large populations of hippocampal place cells during virtual navigation, global remapping during environment changes, and unique responses of place cell ensembles to overhead looming stimulation.

Place fields illuminated.

Place fields illuminated.

The grid-cell normative model: Unifying ‘principles’

A normative model for the emergence of entorhinal grid cells in the brain's navigational system has been proposed (Sorscher et al., 2023. Neuron 111, 121-137). Using computational modeling of place-to-grid cell interactions, the authors characterized the fundamental nature of grid cells through information processing. However, the normative model does not consider certain discoveries that complement or contradict the conditions for such emergence. By briefly reviewing current evidence, we draw some implications on the interplay between place cell replay sequences and intrinsic grid cell oscillations related to the hippocampal-entorhinal navigation system that can extend the normative model.

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.

The Precision of Place Fields Governs Their Fate across Epochs of Experience.

Spatial memories are represented by hippocampal place cells during navigation. This spatial code is dynamic, undergoing changes across time, known as representational drift, and across changes in internal state, even while navigating the same spatial environment with consistent behavior. A dynamic code may provide the hippocampus a means to track distinct epochs of experience that occur at different times or during different internal states and update spatial memories. Changes to the spatial code include place fields (PFs) that remap to new locations and place fields that vanish, while others are stable. However, what determines place field fate across epochs remains unclear. We measured the lap-by-lap properties of place cells in mice during navigation for a block of trials in a rewarded virtual environment. We then determined the position of the place fields in another block of trials in the same spatial environment either separated by a day (a distinct temporal epoch) or during the same session but with reward removed to change reward expectation (a distinct internal state epoch). We found that place cells with remapped place fields across epochs tended to have lower spatial precision during navigation in the initial epoch. Place cells with stable or vanished place fields tended to have higher spatial precision. We conclude that place cells with less precise place fields have greater spatial flexibility, allowing them to respond to, and track, distinct epochs of experience in the same spatial environment, while place cells with precise place fields generally preserve spatial information when their fields reappear.

A neural code for time and space in the human brain

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.

Aversive stimulus-tuned responses in the CA1 of the dorsal hippocampus

Throughout life animals inevitably encounter unforeseen threatening events. Activity of principal cells in the hippocampus is tuned for locations and for salient stimuli in the animals’ environment thus forming a map known to be pivotal for guiding behavior. Here, we explored if a code of threatening stimuli exists in the CA1 region of the dorsal hippocampus of mice by recording neuronal response to aversive stimuli delivered at changing locations. We have discovered a rapidly emerging, location independent response to innoxious aversive stimuli composed of the coordinated activation of subgroups of pyramidal cells and connected interneurons. Activated pyramidal cells had higher basal firing rate, more probably participated in ripples, targeted more interneurons than place cells and many of them lacked place fields. We also detected aversive stimulus-coupled assemblies dominated by the activated neurons. Notably, these assemblies could be observed even before the delivery of the first aversive event. Finally, we uncovered the systematic shift of the spatial code from the aversive to, surprisingly, the reward location during the fearful stimulus. Our results uncovered components of the dorsal CA1 circuit possibly key for re-sculpting the spatial map in response to abrupt aversive events.