What do grid cells contribute to place cell firing?

Retrosplenial inputs drive visual representations in the medial entorhinal cortex

The importance of visual cues for navigation and goal-directed behavior is well established, although the neural mechanisms supporting sensory representations in navigational circuits are largely unknown. Navigation is fundamentally dependent on the medial entorhinal cortex (MEC), which receives direct projections from neocortical visual areas, including the retrosplenial cortex (RSC). Here, we perform high-density recordings of MEC neurons in awake, head-fixed mice presented with simple visual stimuli and assess the dynamics of sensory-evoked activity. We find that a large fraction of neurons exhibit robust responses to visual input. Visually responsive cells are located primarily in layer 3 of the dorsal MEC and can be separated into subgroups based on functional and molecular properties. Furthermore, optogenetic suppression of RSC afferents within the MEC strongly reduces visual responses. Overall, our results demonstrate that the MEC can encode simple visual cues in the environment that may contribute to neural representations of location necessary for accurate navigation.

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the network's balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting "pluripotent" internal neural activity patterns.

Direct cortical inputs to hippocampal area CA1 transmit complementary signals for goal-directed navigation

The subregions of the entorhinal cortex (EC) are conventionally thought to compute dichotomous representations for spatial processing, with the medial EC (MEC) providing a global spatial map and the lateral EC (LEC) encoding specific sensory details of experience. Yet, little is known about the specific types of information EC transmits downstream to the hippocampus. Here, we exploit in vivo sub-cellular imaging to record from EC axons in CA1 while mice perform navigational tasks in virtual reality (VR). We uncover distinct yet overlapping representations of task, location, and context in both MEC and LEC axons. MEC transmitted highly location- and context-specific codes; LEC inputs were biased by ongoing navigational goals. However, during tasks with reliable reward locations, the animals' position could be accurately decoded from either subregion. Our results revise the prevailing dogma about EC information processing, revealing novel ways spatial and non-spatial information is routed and combined upstream of the hippocampus.

Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents

It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.

Grid cells, border cells, and discrete complex analysis

We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the oscillatory models of grid cells. We demonstrate that these models can extend further, producing cells that increase their activity toward the frontiers of the navigated environments. We also construct a network model of neurons with border-bound firing that conforms with the oscillatory models.

Recruitment of grid-like responses in human entorhinal and piriform cortices by odor landmark-based navigation

Olfactory navigation is universal across the animal kingdom. Humans, however, have rarely been considered in this context. Here, we combined olfactometry techniques, virtual reality (VR) software, and neuroimaging methods to investigate whether humans can navigate an olfactory landscape by learning the spatial relationships among discrete odor cues and integrating this knowledge into a spatial map. Our data show that over time, participants improved their performance on the odor navigation task by taking more direct paths toward targets and completing more trials within a given time period. This suggests that humans can successfully navigate a complex odorous environment, reinforcing the notion of human olfactory navigation. fMRI data collected during the olfactory navigation task revealed the emergence of grid-like responses in entorhinal and piriform cortices that were attuned to the same grid orientation. This result implies the existence of a specialized olfactory grid network tasked with guiding spatial navigation based on odor landmarks.

Place and place names: a unified model

The goal of this paper is to offer a unified account of Place as a central theoretical notion across different disciplines. We show that while psychology, geography and other sciences have been converging to a unified view of this notion, linguistics still offers a fragmented perspective. Consequently, place names lack a full-fledged analysis that connects this category to the psychological concept of place. We propose to overcome this impasse by introducing a multi-modal Discourse Representation Theory (DRT) account of place as a conceptual construct and place concepts as specific instances of this construct. We show that current variants of DRT permit us to model place names and their senses, i.e., the meaning(s) that individuals associate with Sydney. We then model non-linguistic place concepts, i.e., the mental representation(s) that individuals can have of the city carrying this name. We present a model of the relation between linguistic meaning and conceptual content via the notion of anchoring relations applied to place. We pair this formal treatment with a morpho-syntactic account of place names building on current generative syntax treatments of proper names. Once we have a morpho-syntactic and semantic model of place names, we use a frame semantics treatment to account for lexical relations among place names. We test the overarching model on a set of recalcitrant problems afflicting current linguistic and multi-disciplinary treatments of place. These are the grammatical complexity and lexical content of place names, place concepts and their networks, and inter-subjective, communicative models of place in discourse. By solving these problems, our account integrates several frameworks (DRT, conceptual analysis, generative syntax, frame semantics) and connects several disciplines (linguistics, psychology, geographic information science, communication models) via a novel, multi-modal account of place. We conclude by discussing the theoretical and empirical import of these results.

Grid Cells, Border Cells and Discrete Complex Analysis

We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the oscillatory models of grid cells. We demonstrate that these models can extend further, producing cells that increase their activity towards the frontiers of the navigated environments. We also construct a network model of neurons with border-bound firing that conforms with the oscillatory models.

Rapid learning of spatial representations for goal-directed navigation based on a novel model of hippocampal place fields

The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior-potentially with implications for rapid learning, quick memory consolidation, and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks, and sometimes require long learning periods. In this paper, we present a self-organizing model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles.

Entorhinal grid-like codes and time-locked network dynamics track others navigating through space

Navigating through crowded, dynamically changing environments requires the ability to keep track of other individuals. Grid cells in the entorhinal cortex are a central component of self-related navigation but whether they also track others' movement is unclear. Here, we propose that entorhinal grid-like codes make an essential contribution to socio-spatial navigation. Sixty human participants underwent functional magnetic resonance imaging (fMRI) while observing and re-tracing different paths of a demonstrator that navigated a virtual reality environment. Results revealed that grid-like codes in the entorhinal cortex tracked the other individual navigating through space. The activity of grid-like codes was time-locked to increases in co-activation and entorhinal-cortical connectivity that included the striatum, the hippocampus, parahippocampal and right posterior parietal cortices. Surprisingly, the grid-related effects during observation were stronger the worse participants performed when subsequently re-tracing the demonstrator's paths. Our findings suggests that network dynamics time-locked to entorhinal grid-cell-related activity might serve to distribute information about the location of others throughout the brain.

Navigational roots of spatial and temporal memory structure

Our minds are constantly in transit, from the present to the past to the future, across places we have and have not directly experienced. Nevertheless, memories of our mental time travel are not organized continuously and are adaptively chunked into contexts and episodes. In this paper, I will review evidence that suggests that spatial boundary representations play a critical role in providing structure to both our spatial and temporal memories. I will illustrate the intimate connection between hippocampal spatial mapping and temporal sequencing of episodic memory to propose that high-level cognitive processes like mental time travel and conceptual mapping are rooted in basic navigational mechanisms that we humans and nonhuman animals share. Our neuroscientific understanding of hippocampal function across species may provide new insight into the origins of even the most uniquely human cognitive abilities.

Excitatory-inhibitory recurrent dynamics produce robust visual grids and stable attractors

Spatially modulated grid cells have been recently found in the rat secondary visual cortex (V2) during active navigation. However, the computational mechanism and functional significance of V2 grid cells remain unknown. To address the knowledge gap, we train a biologically inspired excitatory-inhibitory recurrent neural network to perform a two-dimensional spatial navigation task with multisensory input. We find grid-like responses in both excitatory and inhibitory RNN units, which are robust with respect to spatial cues, dimensionality of visual input, and activation function. Population responses reveal a low-dimensional, torus-like manifold and attractor. We find a link between functional grid clusters with similar receptive fields and structured excitatory-to-excitatory connections. Additionally, multistable torus-like attractors emerged with increasing sparsity in inter- and intra-subnetwork connectivity. Finally, irregular grid patterns are found in recurrent neural network (RNN) units during a visual sequence recognition task. Together, our results suggest common computational mechanisms of V2 grid cells for spatial and non-spatial tasks.

Activation of Dopamine D2 Receptors Alleviates Neuronal Hyperexcitability in the Lateral Entorhinal Cortex via Inhibition of HCN Current in a Rat Model of Chronic Inflammatory Pain

Functional changes in synaptic transmission from the lateral entorhinal cortex to the dentate gyrus (LEC-DG) are considered responsible for the chronification of pain. However, the underlying alterations in fan cells, which are the predominant neurons in the LEC that project to the DG, remain elusive. Here, we investigated possible mechanisms using a rat model of complete Freund's adjuvant (CFA)-induced inflammatory pain. We found a substantial increase in hyperpolarization-activated/cyclic nucleotide-gated currents (Ih), which led to the hyperexcitability of LEC fan cells of CFA slices. This phenomenon was attenuated in CFA slices by activating dopamine D2, but not D1, receptors. Chemogenetic activation of the ventral tegmental area -LEC projection had a D2 receptor-dependent analgesic effect. Intra-LEC microinjection of a D2 receptor agonist also suppressed CFA-induced behavioral hypersensitivity, and this effect was attenuated by pre-activation of the Ih. Our findings suggest that down-regulating the excitability of LEC fan cells through activation of the dopamine D2 receptor may be a strategy for treating chronic inflammatory pain.

A direct excitatory projection from entorhinal layer 6b neurons to the hippocampus contributes to spatial coding and memory

The mammalian hippocampal formation (HF) plays a key role in several higher brain functions, such as spatial coding, learning and memory. Its simple circuit architecture is often viewed as a trisynaptic loop, processing input originating from the superficial layers of the entorhinal cortex (EC) and sending it back to its deeper layers. Here, we show that excitatory neurons in layer 6b of the mouse EC project to all sub-regions comprising the HF and receive input from the CA1, thalamus and claustrum. Furthermore, their output is characterized by unique slow-decaying excitatory postsynaptic currents capable of driving plateau-like potentials in their postsynaptic targets. Optogenetic inhibition of the EC-6b pathway affects spatial coding in CA1 pyramidal neurons, while cell ablation impairs not only acquisition of new spatial memories, but also degradation of previously acquired ones. Our results provide evidence of a functional role for cortical layer 6b neurons in the adult brain.

Morris water maze: a versatile and pertinent tool for assessing spatial learning and memory

Since its development about 40 years ago (1981–2021), Morris water maze has turned into a very popular tool for assessing spatial learning and memory. Its many advantages have ensured its pertinence to date. These include its effectiveness in evaluating hippocampal-dependent learning and memory, exemption from motivational differences across diverse experimental manipulations, reliability in various cross-species studies, and adaptability to many experimental conditions with various test protocols. Nonetheless, throughout its establishment, several experimental and analysis loopholes have galvanized researchers to assess ways in which it could be improved and adapted to fill this gap. Therefore, in this review, we briefly summarize these developments since the early years of its establishment through to the most recent advancements in computerized analysis, offering more comprehensive analysis paradigms. In addition, we discuss the adaptability of the Morris water maze across different test versions and analysis paradigms, providing suggestions with regard to the best paradigms for particular experimental conditions. Hence, the proper selection of the experimental protocols, analysis paradigms, and consideration of the assay’s limitations should be carefully considered. Given that appropriate measures are taken, with various adaptations made, the Morris water maze will likely remain a relevant tool to assess the mechanisms of spatial learning and memory.

Path integration in normal aging and Alzheimer's disease

In this review we discuss converging evidence from human and rodent research demonstrating how path integration (PI) is impaired in healthy aging and Alzheimer's disease (AD), and point to the neural mechanisms that underlie these deficits. Importantly, we highlight that (i) the grid cell network in the entorhinal cortex is crucial for PI in both humans and rodents, (ii) PI deficits are present in healthy aging and are significantly more pronounced in patients with early-stage AD, (iii) compromised entorhinal grid cell computations in healthy older adults and in young adults at risk of AD are linked to PI deficits, and (iv) PI and grid cell deficits may serve as sensitive markers for pathological decline in early AD.

Dynamical self-organization and efficient representation of space by grid cells

To plan trajectories and navigate, animals must maintain a mental representation of the environment and their own position within it. This "cognitive map" is thought to be supported in part by the entorhinal cortex, where grid cells are active when an animal occupies the vertices of a scaling hierarchy of periodic lattices of locations in an enclosure. Here, we review computational developments which suggest that the grid cell network is: (a) efficient, providing required spatial resolution with a minimum number of neurons, (b) self-organizing, dynamically coordinating the structure and scale of the responses, and (c) adaptive, re-organizing in response to changes in landmarks and the structure of the boundaries of spaces. We consider these ideas in light of recent discoveries of similar structures in the mental representation of abstract spaces of shapes and smells, and in other brain areas, and highlight promising directions for future research.

Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.

A computational grid-to-place-cell transformation model indicates a synaptic driver of place cell impairment in early-stage Alzheimer's Disease

Alzheimer's Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.

Biomimetic FPGA-based spatial navigation model with grid cells and place cells

The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons responding to distinct features including location, speed, borders and head direction; an ensuing convergence finally enables navigation and path integration. Here, we report the algorithmic and hardware implementation of biomimetic neural structures encompassing a feed-forward trimodular, multi-layer architecture representing grid-cell, place-cell and decoding modules for navigation. The grid-cell module comprised of neurons that fired in a grid-like pattern, and was built of distinct layers that constituted the dorsoventral span of the medial entorhinal cortex. Each layer was built as an independent continuous attractor network with distinct grid-field spatial scales. The place-cell module comprised of neurons that fired at one or few spatial locations, organized into different clusters based on convergent modular inputs from different grid-cell layers, replicating the gradient in place-field size along the hippocampal dorso-ventral axis. The decoding module, a two-layer neural network that constitutes the convergence of the divergent representations in preceding modules, received inputs from the place-cell module and provided specific coordinates of the navigating object. After vital design optimizations involving all modules, we implemented the tri-modular structure on Zynq Ultrascale+ field-programmable gate array silicon chip, and demonstrated its capacity in precisely estimating the navigational trajectory with minimal overall resource consumption involving a mere 2.92% Look Up Table utilization. Our implementation of a biomimetic, digital spatial navigation system is stable, reliable, reconfigurable, real-time with execution time of about 32 s for 100k input samples (in contrast to 40 minutes on Intel Core i7-7700 CPU with 8 cores clocking at 3.60 GHz) and thus can be deployed for autonomous-robotic navigation without requiring additional sensors.

2-Pentadecyl-2-oxazoline ameliorates memory impairment and depression-like behaviour in neuropathic mice: possible role of adrenergic alpha2- and H3 histamine autoreceptors

Neuropathic pain (NP) remains an untreatable disease due to the complex pathophysiology that involves the whole pain neuraxis including the forebrain. Sensory dysfunctions such as allodynia and hyperalgesia are only part of the symptoms associated with neuropathic pain that extend to memory and affectivity deficits. The development of multi-target molecules might be a promising therapeutic strategy against the symptoms associated with NP. 2-pentadecyl-2-oxazoline (PEA-OXA) is a plant-derived agent, which has shown effectiveness against chronic pain and associated neuropsychiatric disorders. The molecular mechanisms by which PEA-OXA exerts its effects are, however, only partially known. In the current study, we show that PEA-OXA, besides being an alpha2 adrenergic receptor antagonist, also acts as a modulator at histamine H3 receptors, and report data on its effects on sensory, affective and cognitive symptoms associated with the spared nerve injury (SNI) model of neuropathic pain in mice. Treatment for 14 days with PEA-OXA after the onset of the symptoms associated with neuropathic pain resulted in the following effects: (i) allodynia was decreased; (ii) affective/cognitive impairment associated with SNI (depression, spatial, and working memories) was counteracted; (iii) long-term potentiation in vivo in the lateral entorhinal cortex-dentate gyrus (perforant pathway, LPP) was ameliorated, (iv) hippocampal glutamate, GABA, histamine, norepinephrine and dopamine level alterations after peripheral nerve injury were reversed, (v) expression level of the TH positive neurons in the Locus Coeruleus were normalized. Thus, a 16-day treatment with PEA-OXA alleviates the sensory, emotional, cognitive, electrophysiological and neurochemical alterations associated with SNI-induced neuropathic pain.

Structuring Knowledge with Cognitive Maps and Cognitive Graphs

Humans and animals use mental representations of the spatial structure of the world to navigate. The classical view is that these representations take the form of Euclidean cognitive maps, but alternative theories suggest that they are cognitive graphs consisting of locations connected by paths. We review evidence suggesting that both map-like and graph-like representations exist in the mind/brain that rely on partially overlapping neural systems. Maps and graphs can operate simultaneously or separately, and they may be applied to both spatial and nonspatial knowledge. By providing structural frameworks for complex information, cognitive maps and cognitive graphs may provide fundamental organizing schemata that allow us to navigate in physical, social, and conceptual spaces.

A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells

Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.

Fate of Duplicated Neural Structures

Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.

Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex

Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time.

Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called transition scale-space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally [Formula: see text] for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented: top down and bottom up. The two modes are evaluated in symbolic simulations (i.e., without biologically plausible spiking neurons). Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the results are discussed in depth with respect to biological plausibility, and several testable predictions are derived. Moreover, relations to other grid cell models, multiresolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of the medial entorhinal cortex. Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge.

Source code for all simulations presented in this paper can be found at https://github.com/rochus/transitionscalespace .

The Neurocognitive Basis of Spatial Reorientation

The ability to recover one's bearings when lost is a skill that is fundamental for spatial navigation. We review the cognitive and neural mechanisms that underlie this ability, with the aim of linking together previously disparate findings from animal behavior, human psychology, electrophysiology, and cognitive neuroscience. Behavioral work suggests that reorientation involves two key abilities: first, the recovery of a spatial reference frame (a cognitive map) that is appropriate to the current environment; and second, the determination of one's heading and location relative to that reference frame. Electrophysiological recording studies, primarily in rodents, have revealed potential correlates of these operations in place, grid, border/boundary, and head-direction cells in the hippocampal formation. Cognitive neuroscience studies, primarily in humans, suggest that the perceptual inputs necessary for these operations are processed by neocortical regions such as the retrosplenial complex, occipital place area and parahippocampal place area, with the retrosplenial complex mediating spatial transformations between the local environment and the recovered spatial reference frame, the occipital place area supporting perception of local boundaries, and the parahippocampal place area processing visual information that is essential for identification of the local spatial context. By combining results across these various literatures, we converge on a unified account of reorientation that bridges the cognitive and neural domains.

The Neurobiology of Mammalian Navigation

Mammals have evolved specialized brain systems to support efficient navigation within diverse habitats and over varied distances, but while navigational strategies and sensory mechanisms vary across species, core spatial components appear to be widely shared. This review presents common elements found in mammalian spatial mapping systems, focusing on the cells in the hippocampal formation representing orientational and locational spatial information, and 'core' mammalian hippocampal circuitry. Mammalian spatial mapping systems make use of both allothetic cues (space-defining cues in the external environment) and idiothetic cues (cues derived from self-motion). As examples of each cue type, we discuss: environmental boundaries, which control both orientational and locational neuronal activity and behaviour; and 'path integration', a process that allows the estimation of linear translation from velocity signals, thought to depend upon grid cells in the entorhinal cortex. Building cognitive maps entails sampling environments: we consider how the mapping system controls exploration to acquire spatial information, and how exploratory strategies may integrate idiothetic with allothetic information. We discuss how 'replay' may act to consolidate spatial maps, and simulate trajectories to aid navigational planning. Finally, we discuss grid cell models of vector navigation.

Navigating with grid and place cells in cluttered environments

Hippocampal formation contains several classes of neurons thought to be involved in navigational processes, in particular place cells and grid cells. Place cells have been associated with a topological strategy for navigation, while grid cells have been suggested to support metric vector navigation. Grid cell‐based vector navigation can support novel shortcuts across unexplored territory by providing the direction toward the goal. However, this strategy is insufficient in natural environments cluttered with obstacles. Here, we show how navigation in complex environments can be supported by integrating a grid cell‐based vector navigation mechanism with local obstacle avoidance mediated by border cells and place cells whose interconnections form an experience‐dependent topological graph of the environment. When vector navigation and object avoidance fail (i.e., the agent gets stuck), place cell replay events set closer subgoals for vector navigation. We demonstrate that this combined navigation model can successfully traverse environments cluttered by obstacles and is particularly useful where the environment is underexplored. Finally, we show that the model enables the simulated agent to successfully navigate experimental maze environments from the animal literature on cognitive mapping. The proposed model is sufficiently flexible to support navigation in different environments, and may inform the design of experiments to relate different navigational abilities to place, grid, and border cell firing.

Grid-like Neural Representations Support Olfactory Navigation of a Two-Dimensional Odor Space

Searching for food, friends, and mates often begins with an airborne scent. Importantly, odor concentration rises with physical proximity to an odorous source, suggesting a framework for orienting within olfactory landscapes to optimize behavior. Here, we created a two-dimensional odor space composed purely of odor stimuli to model how a navigator encounters smells in a natural environment. We show that human subjects can learn to navigate in olfactory space and form predictions of to-be-encountered smells. During navigation, fMRI responses in entorhinal cortex and ventromedial prefrontal cortex take the form of grid-like representations with hexagonal periodicity and entorhinal grid strength scaled with behavioral performance across subjects. The identification of olfactory grid-like codes with 6-fold symmetry highlights a unique neural mechanism by which odor information can be assembled into spatially navigable cognitive maps, optimizing orientation, and path finding toward an odor source.

Metabotropic Glutamate Receptor 5 and 8 Modulate the Ameliorative Effect of Ultramicronized Palmitoylethanolamide on Cognitive Decline Associated with Neuropathic Pain

This study investigated whether metabotropic glutamate receptor (mGluR) 5 and 8 are involved in the effect of ultramicronizedpalmitoylethanolamide (um-PEA) on the cognitive behavior and long term potentiation (LTP) at entorhinal cortex (LEC)-dentate gyrus (DG) pathway in mice rendered neuropathic by the spare nerve injury (SNI). SNI reduced discriminative memory and LTP. Um-PEA treatment started after the development of neuropathic pain had no effects in sham mice, whereas it restored cognitive behavior and LTP in SNI mice. 2-Methyl-6-(phenylethynyl) pyridine (MPEP), a selective mGluR5 antagonist, improved cognition in SNI mice and produced a chemical long term depression of the field excitatory postsynaptic potentials (fEPSPs) in sham and SNI mice. After theta burst stimulation (TBS) MPEP restored LTP in SNI mice. In combination with PEA, MPEP antagonized the PEA effect on discriminative memory and decreased LTP in SNI mice. The (RS)-4-(1-amino-1-carboxyethyl)phthalic acid (MDCPG), a selective mGluR8 antagonist, did not affect discriminative memory, but it induced a chemical LTP and prevented the enhancement of fEPSPs after TBS in SNI mice which were treated or not treated with PEA. The effect of PEA on LTP and cognitive behavior was modulated by mGluR5 and mGluR8. In particular in the SNI conditions, the mGluR5 blockade facilitated memory and LTP, but prevented the beneficial effects of PEA on discriminative memory while the mGluR8 blockade, which was ineffective in itself, prevented the favorable action of the PEA on LTP. Thus, although their opposite roles (excitatory/inhibitory of the two receptor subtypes on the glutamatergic system), they appeared to be required for the neuroprotective effect of PEA in conditions of neuropathic pain.

Hippocampal place cell encoding of sloping terrain

Effective navigation relies on knowledge of one's environment. A challenge to effective navigation is accounting for the time and energy costs of routes. Irregular terrain in ecological environments poses a difficult navigational problem as organisms ought to avoid effortful slopes to minimize travel costs. Route planning and navigation have previously been shown to involve hippocampal place cells and their ability to encode and store information about an organism's environment. However, little is known about how place cells may encode the slope of space and associated energy costs as experiments are traditionally carried out in flat, horizontal environments. We set out to investigate how dorsal-CA1 place cells in rats encode systematic changes to the slope of an environment by tilting a shuttle box from flat to 15 ° and 25 ° while minimizing external cue change. Overall, place cell encoding of tilted space was as robust as their encoding of flat ground as measured by traditional place cell metrics such as firing rates, spatial information, coherence, and field size. A large majority of place cells did, however, respond to slope by undergoing partial, complex remapping when the environment was shifted from one tilt angle to another. The propensity for place cells to remap did not, however, depend on the vertical distance the field shifted. Changes in slope also altered the temporal coding of information as measured by the rate of theta phase precession of place cell spikes, which decreased with increasing tilt angles. Together these observations indicate that place cells are sensitive to relatively small changes in terrain slope and that terrain slope may be an important source of information for organizing place cell ensembles. The terrain slope information encoded by place cells could be utilized by efferent regions to determine energetically advantageous routes to goal locations.

Environmental deformations dynamically shift the grid cell spatial metric

In familiar environments, the firing fields of entorhinal grid cells form regular triangular lattices. However, when the geometric shape of the environment is deformed, these time-averaged grid patterns are distorted in a grid scale-dependent and local manner. We hypothesized that this distortion in part reflects dynamic anchoring of the grid code to displaced boundaries, possibly through border cell-grid cell interactions. To test this hypothesis, we first reanalyzed two existing rodent grid rescaling datasets to identify previously unrecognized boundary-tethered shifts in grid phase that contribute to the appearance of rescaling. We then demonstrated in a computational model that boundary-tethered phase shifts, as well as scale-dependent and local distortions of the time-averaged grid pattern, could emerge from border-grid interactions without altering inherent grid scale. Together, these results demonstrate that environmental deformations induce history-dependent shifts in grid phase, and implicate border-grid interactions as a potential mechanism underlying these dynamics.

Ultra-micronized palmitoylethanolamide rescues the cognitive decline-associated loss of neural plasticity in the neuropathic mouse entorhinal cortex-dentate gyrus pathway

Chronic pain is associated with cognitive deficits. Palmitoylethanolamide (PEA) has been shown to ameliorate pain and pain-related cognitive impairments by restoring glutamatergic synapses functioning in the spared nerve injury (SNI) of the sciatic nerve in mice. SNI reduced mechanical and thermal threshold, spatial memory and LTP at the lateral entorhinal cortex (LEC)-dentate gyrus (DG) pathway. It decreased also postsynaptic density, volume and dendrite arborization of DG and increased the expression of metabotropic glutamate receptor 1 and 7 (mGluR1 and mGluR7), of the GluR1, GluR1s845 and GluR1s831 subunits of AMPA receptor and the levels of glutamate in the DG. The level of the endocannabinoid 2-arachidonoylglycerol (2-AG) was instead increased in the LEC. Chronic treatment with PEA, starting from when neuropathic pain was fully developed, was able to reverse mechanical allodynia and thermal hyperalgesia, memory deficit and LTP in SNI wild type, but not in PPARα null, mice. PEA also restored the level of glutamate and the expression of phosphorylated GluR1 subunits, postsynaptic density and neurogenesis. Altogether, these results suggest that neuropathic pain negatively affects cognitive behavior and related LTP, glutamatergic synapse and synaptogenesis in the DG. In these conditions PEA treatment alleviates pain and cognitive impairment by restoring LTP and synaptic maladaptative changes in the LEC-DG pathway. These outcomes open new perspectives for the use of the N-acylethanolamines, such as PEA, for the treatment of neuropathic pain and its central behavioural sequelae.

How the Hippocampus Represents Memories: Making Sense of Memory Allocation Studies

In recent years there has been a wealth of studies investigating how memories are allocated in the hippocampus. Some of those studies showed that it is possible to manipulate the identity of neurons recruited to represent a given memory without affecting the memory's behavioral expression. Those findings raised questions about how the hippocampus represents memories, with some researchers arguing that hippocampal neurons do not represent fixed stimuli. Herein, an alternative hypothesis is argued. Neurons in high-order brain regions can be tuned to multiple dimensions, forming complex, abstract representations. It is argued that such complex receptive fields allow those neurons to show some flexibility in their responses while still representing relatively fixed sets of stimuli. Moreover, it is pointed out that changes induced by artificial manipulation of cell assemblies are not completely redundant-the observed behavioral redundancy does not imply cognitive redundancy, as different, but similar, memories may induce the same behavior.

A neural-level model of spatial memory and imagery

We present a model of how neural representations of egocentric spatial experiences in parietal cortex interface with viewpoint-independent representations in medial temporal areas, via retrosplenial cortex, to enable many key aspects of spatial cognition. This account shows how previously reported neural responses (place, head-direction and grid cells, allocentric boundary- and object-vector cells, gain-field neurons) can map onto higher cognitive function in a modular way, and predicts new cell types (egocentric and head-direction-modulated boundary- and object-vector cells). The model predicts how these neural populations should interact across multiple brain regions to support spatial memory, scene construction, novelty-detection, 'trace cells', and mental navigation. Simulated behavior and firing rate maps are compared to experimental data, for example showing how object-vector cells allow items to be remembered within a contextual representation based on environmental boundaries, and how grid cells could update the viewpoint in imagery during planning and short-cutting by driving sequential place cell activity.

Integration and multiplexing of positional and contextual information by the hippocampal network

The hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations (“teleportation” experiment of Jezek et al). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams.

As an animal moves in space and receives external sensory inputs, it must dynamically maintain the representations of its position and environment at all times. How the hippocampus, the brain area crucial for spatial representations, achieves this task, and manages possible conflicts between different inputs remains unclear. We propose here a comprehensive attractor neural network-based model of the hippocampus and of its multiple input streams (including self-motion). We show that this model is capable of maintaining faithful representations of positional and contextual information, and resolves conflicts by adapting internal representations to match external cues. Model predictions are confirmed by the detailed analysis of hippocampal recordings of a rat submitted to quickly varying and conflicting contextual inputs.

A unified hierarchical oscillatory network model of head direction cells, spatially periodic cells, and place cells

Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the Velocity Driven Oscillatory Network (VDON) and Locomotor Driven Oscillatory Network. Both models have basically three stages in common such as direction encoding stage, path integration (PI) stage, and a stage of unsupervised learning of PI values. In the first model, the following three stages are implemented: head direction layer, frequency modulation by a layer of oscillatory neurons, and an unsupervised stage that extracts the principal components from the oscillator outputs. In the second model, a refined version of the first model, the stages are extraction of velocity representation from the locomotor input, frequency modulation by a layer of oscillators, and two cascaded unsupervised stages consisting of the lateral anti-hebbian network. The principal component stage of VDON exhibits grid cell-like spatially periodic responses including hexagonal firing fields. Locomotor Driven Oscillatory Network shows the emergence of spatially periodic grid cells and periodically active border-like cells in its lower layer; place cell responses are found in its higher layer. This model shows the inheritance of phase precession from grid cell to place cell in both one- and two-dimensional spaces. It also shows a novel result on the influence of locomotion rhythms on the grid cell activity. The study thus presents a comprehensive, unifying hierarchical model for hippocampal spatial cells.

Heterogeneity in hippocampal place coding

The discovery of place cells provided fundamental insight into the neural basis by which the hippocampus encodes spatial memories and supports navigation and prompted the development of computational models to explain the emergence of their spatial selectively. Many such works posit that input from entorhinal grid cells is critical to the formation of place fields, a prediction that has received mixed experimental support. Potentially reconciling seemingly conflicting findings is recent work indicating that subpopulations of pyramidal neurons are functionally distinct and may be driven to varying degrees by different inputs. Additionally, new studies have demonstrated that hippocampal principal neurons encode a myriad of features extending beyond current position. Here, we highlight recent evidence for how extensive heterogeneity in connectivity and genetic expression could interact with membrane biophysics to enable place cells to encode a diverse range of stimuli. These recent findings highlight the need for more computational models that integrate these heterogeneous features of hippocampal principal neurons.

Lesions of the Head Direction Cell System Increase Hippocampal Place Field Repetition

A central tenet of systems neuroscience is that the mammalian hippocampus provides a cognitive map of the environment. This view is supported by the finding of place cells, neurons whose firing is tuned to specific locations in an animal's environment, within this brain region. Recent work, however, has shown that these cells repeat their firing fields across visually identical maze compartments [1, 2]. This repetition is not observed if these compartments face different directions, suggesting that place cells use a directional input to differentiate otherwise similar local environments [3, 4]. A clear candidate for this input is the head direction cell system. To test this, we disrupted the head direction cell system by lesioning the lateral mammillary nuclei and then recorded place cells as rats explored multiple, connected compartments, oriented in the same or in different directions. As shown previously, we found that place cells in control animals exhibited repeated fields in compartments arranged in parallel, but not in compartments facing different directions. In contrast, the place cells of animals with lesions of the head direction cell system exhibited repeating fields in both conditions. Thus, directional information provided by the head direction cell system appears essential for the angular disambiguation by place cells of visually identical compartments.

From grid cells to place cells with realistic field sizes

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.

Place and Grid Cells in a Loop: Implications for Memory Function and Spatial Coding

Place cells in the hippocampus and grid cells in the medial entorhinal cortex have different codes for space. However, how one code relates to the other is ill understood. Based on the anatomy of the entorhinal-hippocampal circuitry, we constructed a model of place and grid cells organized in a loop to investigate their mutual influence in the establishment of their codes for space. Using computer simulations, we first replicated experiments in rats that measured place and grid cell activity in different environments, and then assessed which features of the model account for different phenomena observed in neurophysiological data, such as pattern completion and pattern separation, global and rate remapping of place cells, and realignment of grid cells. We found that (1) the interaction between grid and place cells converges quickly; (2) the spatial code of place cells does not require, but is altered by, grid cell input; (3) plasticity in sensory inputs to place cells is key for pattern completion but not pattern separation; (4) grid realignment can be explained in terms of place cell remapping as opposed to the other way around; (5) the switch between global and rate remapping is self-organized; and (6) grid cell input to place cells helps stabilize their code under noisy and/or inconsistent sensory input. We conclude that the hippocampus-entorhinal circuit uses the mutual interaction of place and grid cells to encode the surrounding environment and propose a theory on how such interdependence underlies the formation and use of the cognitive map.SIGNIFICANCE STATEMENT The mammalian brain implements a positional system with two key pieces: place and grid cells. To gain insight into the dynamics of place and grid cell interaction, we built a computational model with the two cell types organized in a loop. The proposed model accounts for differences in how place and grid cells represent different environments and provides a new interpretation in which place and grid cells mutually interact to form a coupled code for space.

Models of spatial and temporal dimensions of memory

Episodic memory involves coding of the spatial location and time of individual events. Coding of space and time is also relevant to working memory, spatial navigation, and the disambiguation of overlapping memory representations. Neurophysiological data demonstrate that neuronal activity codes the current, past and future location of an animal as well as temporal intervals within a task. Models have addressed how neural coding of space and time for memory function could arise, with both dimensions coded by the same neurons. Neural coding could depend upon network oscillatory and attractor dynamics as well as modulation of neuronal intrinsic properties. These models are relevant to the coding of space and time involving structures including the hippocampus, entorhinal cortex, retrosplenial cortex, striatum and parahippocampal gyrus, which have been implicated in both animal and human studies.