Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties

Goal-directed navigation requires continuously integrating uncertain self-motion and landmark cues into an internal sense of location and direction, concurrently planning future paths, and sequentially executing motor actions. Here, we provide a unified account of these processes with a computational model of probabilistic path planning in the framework of optimal feedback control under uncertainty. This model gives rise to diverse human navigational strategies previously believed to be distinct behaviors and predicts quantitatively both the errors and the variability of navigation across numerous experiments. This furthermore explains how sequential egocentric landmark observations form an uncertain allocentric cognitive map, how this internal map is used both in route planning and during execution of movements, and reconciles seemingly contradictory results about cue-integration behavior in navigation. Taken together, the present work provides a parsimonious explanation of how patterns of human goal-directed navigation behavior arise from the continuous and dynamic interactions of spatial uncertainties in perception, cognition, and action.

Control and recalibration of path integration in place cells using optic flow

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Taking a shortcut: what mechanisms do fish use?

Path integration is a powerful navigational mechanism whereby individuals continuously update their distance and angular vector of movement to calculate their position in relation to their departure location, allowing them to return along the most direct route even across unfamiliar terrain. While path integration has been investigated in several terrestrial animals, it has never been demonstrated in aquatic vertebrates, where movement occurs through volumetric space and sensory cues available for navigation are likely to differ substantially from those in terrestrial environments. By performing displacement experiments with Lamprologus ocellatus, we show evidence consistent with fish using path integration to navigate alongside other mechanisms (allothetic place cues and route recapitulation). These results indicate that the use of path integration is likely to be deeply rooted within the vertebrate phylogeny irrespective of the environment, and suggests that fish may possess a spatial encoding system that parallels that of mammals.

Spontaneous Dynamics of Hippocampal Place Fields in a Model of Combinatorial Competition among Stable Inputs

We present computer simulations illustrating how the plastic integration of spatially stable inputs could contribute to the dynamic character of hippocampal spatial representations. In novel environments of slightly larger size than typical apparatus, the emergence of well-defined place fields in real place cells seems to rely on inputs from normally functioning grid cells. Theoretically, the grid-to-place transformation is possible if a place cell is able to respond selectively to a combination of suitably aligned grids. We previously identified the functional characteristics that allow a synaptic plasticity rule to accomplish this selection by synaptic competition during rat foraging behavior. Here, we show that the synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time, when the model is run in larger environments and the topographical/modular organization of grid inputs is taken into account. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization-ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models. In sum, spontaneous remapping could arise from rapid synaptic learning involving inputs that are functionally homogeneous, spatially stable, and minimally stochastic.

The mosaic structure of the mammalian cognitive map

The cognitive map, proposed by Tolman in the 1940s, is a hypothetical internal representation of space constructed by the brain to enable an animal to undertake flexible spatial behaviors such as navigation. The subsequent discovery of place cells in the hippocampus of rats suggested that such a map-like representation does exist, and also provided a tool with which to explore its properties. Single-neuron studies in rodents conducted in small singular spaces have suggested that the map is founded on a metric framework, preserving distances and directions in an abstract representational format. An open question is whether this metric structure pertains over extended, often complexly structured real-world space. The data reviewed here suggest that this is not the case. The emerging picture is that instead of being a single, unified construct, the map is a mosaic of fragments that are heterogeneous, variably metric, multiply scaled, and sometimes laid on top of each other. Important organizing factors within and between fragments include boundaries, context, compass direction, and gravity. The map functions not to provide a comprehensive and precise rendering of the environment but rather to support adaptive behavior, tailored to the species and situation.

Multiplexing of temporal and spatial information in the lateral entorhinal cortex

Episodic memory involves the processing of spatial and temporal aspects of personal experiences. The lateral entorhinal cortex (LEC) plays an essential role in subserving memory. However, the specific mechanism by which LEC integrates spatial and temporal information remains elusive. Here, we recorded LEC neurons while rats performed foraging and shuttling behaviors on one-dimensional, linear or circular tracks. Unlike open-field foraging tasks, many LEC cells displayed spatial firing fields in these tasks and demonstrated selectivity for traveling directions. Furthermore, some LEC neurons displayed changes in the firing rates of their spatial rate maps during a session, a phenomenon referred to as rate remapping. Importantly, this temporal modulation was consistent across sessions, even when the spatial environment was altered. Notably, the strength of temporal modulation was found to be greater in LEC compared to other brain regions, such as the medial entorhinal cortex (MEC), CA1, and CA3. Thus, the spatial rate mapping observed in LEC neurons may serve as a coding mechanism for temporal context, allowing for flexible multiplexing of spatial and temporal information.

Hippocampal firing fields anchored to a moving object predict homing direction during path-integration-based behavior

Homing based on path integration (H-PI) is a form of navigation in which an animal uses self-motion cues to keep track of its position and return to a starting point. Despite evidence for a role of the hippocampus in homing behavior, the hippocampal spatial representations associated with H-PI are largely unknown. Here we developed a homing task (AutoPI task) that required a mouse to find a randomly placed lever on an arena before returning to its home base. Recordings from the CA1 area in male mice showed that hippocampal neurons remap between random foraging and AutoPI task, between trials in light and dark conditions, and between search and homing behavior. During the AutoPI task, approximately 25% of the firing fields were anchored to the lever position. The activity of 24% of the cells with a lever-anchored field predicted the homing direction of the animal on each trial. Our results demonstrate that the activity of hippocampal neurons with object-anchored firing fields predicts homing behavior.

Grid Cell Firing Patterns Maintain their Hexagonal Firing Patterns on a Circular Track

In an open two-dimensional environment, grid cells in the medial entorhinal cortex are known to be active in multiple locations, displaying a striking periodic hexagonal firing pattern covering the entire space. Both modeling and experimental data suggest that such periodic spatial representations may emerge from a continuous attractor network. According to this theory, grid cell activity in any stable 1D environment is a slice through an underlying 2D hexagonal pattern, which is supported by some experimental studies but challenged by others. Grid cells are believed to play a fundamental role in path integration, and so understanding their behavior in various environments is crucial for understanding the flow of information through the entorhinal-hippocampal system. To this end, we analyzed the activity of grid cells when rats traversed a circular track. A previous study involving this data set analyzed individual grid cell activity patterns separately, but we found that individual grid cells do not provide sufficient data for determining the underlying spatial activity pattern. To circumvent this, we compute the population autocorrelation, which pools together population responses from all grid cells within the same module. This novel approach recovers the underlying six-peak hexagonal pattern that was not observable in the individual autocorrelations. We also use the population autocorrelation to infer the spacing and orientation of the population lattice, revealing how the lattice differs across environments. Furthermore, the population autocorrelation of the linearized track reveals that at the level of the population, grid cells have an allocentric code for space. These results are strong support for the attractor network theory for grid cells, and our novel approach can be used to analyze grid cell activity in any undersampled environment.

Grid Cell Percolation

Grid cells play a principal role in enabling cognitive representations of ambient environments. The key property of these cells-the regular arrangement of their firing fields-is commonly viewed as a means for establishing spatial scales or encoding specific locations. However, using grid cells' spiking outputs for deducing geometric orderliness proves to be a strenuous task due to fairly irregular activation patterns triggered by the animal's sporadic visits to the grid fields. This article addresses statistical mechanisms enabling emergent regularity of grid cell firing activity from the perspective of percolation theory. Using percolation phenomena for modeling the effect of the rat's moves through the lattices of firing fields sheds new light on the mechanisms of spatial information processing, spatial learning, path integration, and establishing spatial metrics. It is also shown that physiological parameters required for spiking percolation match the experimental range, including the characteristic 2/3 ratio between the grid fields' size and the grid spacing, pointing at a biological viability of the approach.

Spontaneous dynamics of hippocampal place fields in a model of combinatorial competition among stable inputs

We present computer simulations illustrating how the plastic integration of spatially stable inputs could contribute to the dynamic character of hippocampal spatial representations. In novel environments of slightly larger size than typical apparatus, the emergence of well-defined place fields in real place cells seems to rely on inputs from normally functioning grid cells. Theoretically, the grid-to-place transformation is possible if a place cell is able to respond selectively to a combination of suitably aligned grids. We previously identified the functional characteristics that allow a synaptic plasticity rule to accomplish this selection by synaptic competition during rat foraging behavior. Here, we show that the synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time, when the model is run in larger environments and the topographical/modular organization of grid inputs is taken into account. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization-ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models. In sum, spontaneous remapping could arise from rapid synaptic learning involving inputs that are functionally homogeneous, spatially stable, and minimally stochastic.

Significance Statement

In both AI and theoretical neuroscience, learning systems often rely on the asymptotic convergence of slow-acting learning rules applied to input spaces that are presumed to be sampled repeatedly, for example over developmental timescales. Place cells of the hippocampus testify to a neural system capable of rapidly encoding cognitive variables-such as the animal's position in space-from limited experience. These internal representations undergo "spontaneous" changes over time, spurring much interest in their cognitive significance and underlying mechanisms. We investigate a model suggesting that some of these changes could be a tradeoff of rapid learning.

Different Types of Survey-Based Environmental Representations: Egocentric vs. Allocentric Cognitive Maps

The goal of the current study was to show the existence of distinct types of survey-based environmental representations, egocentric and allocentric, and provide experimental evidence that they are formed by different types of navigational strategies, path integration and map-based navigation, respectively. After traversing an unfamiliar route, participants were either disoriented and asked to point to non-visible landmarks encountered on the route (Experiment 1) or presented with a secondary spatial working memory task while determining the spatial locations of objects on the route (Experiment 2). The results demonstrate a double dissociation between the navigational strategies underlying the formation of allocentric and egocentric survey-based representation. Specifically, only the individuals who generated egocentric survey-based representations of the route were affected by disorientation, suggesting they relied primarily on a path integration strategy combined with landmark/scene processing at each route segment. In contrast, only allocentric-survey mappers were affected by the secondary spatial working memory task, suggesting their use of map-based navigation. This research is the first to show that path integration, in conjunction with egocentric landmark processing, is a distinct standalone navigational strategy underpinning the formation of a unique type of environmental representation-the egocentric survey-based representation.

Cerebellar control of a unitary head direction sense

The head-direction (HD) system, a key neural circuit for navigation, consists of several anatomical structures containing neurons selective to the animal's head direction. HD cells exhibit ubiquitous temporal coordination across brain regions, independently of the animal's behavioral state or sensory inputs. Such temporal coordination mediates a single, stable, and persistent HD signal, which is essential for intact orientation. However, the mechanistic processes behind the temporal organization of HD cells are unknown. By manipulating the cerebellum, we identify pairs of HD cells recorded from two brain structures (anterodorsal thalamus and retrosplenial cortex) that lose their temporal coordination, specifically during the removal of the external sensory inputs. Further, we identify distinct cerebellar mechanisms that participate in the spatial stability of the HD signal depending on sensory signals. We show that while cerebellar protein phosphatase 2B-dependent mechanisms facilitate the anchoring of the HD signal on the external cues, the cerebellar protein kinase C-dependent mechanisms are required for the stability of the HD signal by self-motion cues. These results indicate that the cerebellum contributes to the preservation of a single and stable sense of direction.

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.

From representations to servomechanisms to oscillators: my journey in the study of cognition

The study of comparative cognition bloomed in the 1970s and 1980s with a focus on representations in the heads of animals that undergird what animals can achieve. Even in action-packed domains such as navigation and spatial cognition, a focus on representations prevailed. In the 1990s, I suggested a conception of navigation in terms of navigational servomechanisms. A servomechanism can be said to aim for a goal, with deviations from the goal-directed path registering as an error. The error drives action to reduce the error in a negative-feedback loop. This loop, with the action reducing the very signal that drove action in the first place, is key to defining a servomechanism. Even though actions are crucial components of servomechanisms, my focus was on the representational component that encodes signals and evaluates errors. Recently, I modified and amplified this view in claiming that, in navigation, servomechanisms operate by modulating the performance of oscillators, endogenous units that produce periodic action. The pattern is found from bacteria travelling micrometres to sea turtles travelling thousands of kilometres. This pattern of servomechanisms working with oscillators is found in other realms of cognition and of life. I think that oscillators provide an effective way to organise an organism's own activities while servomechanisms provide an effective means to adjust to the organism's environment, including that of its own body.

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.

No Need for Landmarks: An Embodied Neural Controller for Robust Insect-Like Navigation Behaviors

Bayesian filters have been considered to help refine and develop theoretical views on spatial cell functions for self-localization. However, extending a Bayesian filter to reproduce insect-like navigation behaviors (e.g., home searching) remains an open and challenging problem. To address this problem, we propose an embodied neural controller for self-localization, foraging, backward homing (BH), and home searching of an advanced mobility sensor (AMOS)-driven insect-like robot. The controller, comprising a navigation module for the Bayesian self-localization and goal-directed control of AMOS and a locomotion module for coordinating the 18 joints of AMOS, leads to its robust insect-like navigation behaviors. As a result, the proposed controller enables AMOS to perform robust foraging, BH, and home searching against various levels of sensory noise, compared to conventional controllers. Its implementation relies only on self-localization and heading perception, rather than global positioning and landmark guidance. Interestingly, the proposed controller makes AMOS achieve spiral searching patterns comparable to those performed by real insects. We also demonstrated the performance of the controller for real-time indoor and outdoor navigation in a real insect-like robot without any landmark and cognitive map.

The hearing hippocampus

The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.

The neural bases for timing of durations

Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future ('prospective timing') and durations that start in the past and end either in the past or the present ('retrospective timing'). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration 'estimation' are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.

Experience-driven rate modulation is reinstated during hippocampal replay

Replay, the sequential reactivation within a neuronal ensemble, is a central hippocampal mechanism postulated to drive memory processing. While both rate and place representations are used by hippocampal place cells to encode behavioral episodes, replay has been largely defined by only the latter – based on the fidelity of sequential activity across neighboring place fields. Here, we show that dorsal CA1 place cells in rats can modulate their firing rate between replay events of two different contexts. This experience-dependent phenomenon mirrors the same pattern of rate modulation observed during behavior and can be used independently from place information within replay sequences to discriminate between contexts. Our results reveal the existence of two complementary neural representations available for memory processes.

How do our brains store memories? We now know that this is a complex and dynamic process, involving multiple regions of the brain. A brain region, called the hippocampus, plays an important role in memory formation. While we sleep, the hippocampus works to consolidate information, and eventually creates stable, long-term memories that are then stored in other parts of the brain.

But how does the hippocampus do this? Neuroscientists believe that it can replay the patterns of brain activity that represent particular memories. By repeatedly doing this while we sleep, the hippocampus can then direct the transfer of this information to the rest of the brain for storage.

The behaviour of nerve cells in the brain underpins these patterns of brain activity. When a nerve cell is active, it fires tiny electrical impulses that can be detected experimentally. The brain thus represents information in two ways: which nerve cells are active and when (sequential patterns); and how active the nerve cells are (how fast they fire electrical impulses or firing rate). For example, when an animal moves from one location to another, special place cells in the hippocampus become active in a distinct sequence. Depending on the context, they will also fire faster or slower.

We know that the hippocampus can replay sequential patterns of nerve cell activity during memory consolidation, but whether it can also replay the firing rates associated with a particular experience is still unknown. Tirole, Huelin Gorriz et al. set out to determine if the hippocampus could also preserve the information encoded by firing rate during replay.

In the experiments, rats explored two different environments that they had not seen before. The activity of the rats’ place cells was recorded before and after they explored, and also later while they were sleeping. Analysis of the recordings revealed that during replay, the rats’ hippocampi could indeed reproduce both the sequential patterns of activity and the firing rate of the place cells. It also confirmed that each environment was associated with unique firing rates – in other words, the firing rates were memory-specific.

These results contribute to our understanding of how the hippocampus represents and processes information about our experiences. More broadly, they also shed new light on how the brain lays down memories, by revealing a key part of the mechanism that it uses to consolidate that information.

A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Spatialization of Time in the Entorhinal-Hippocampal System

The functional role of the entorhinal-hippocampal system has been a long withstanding mystery. One key theory that has become most popular is that the entorhinal-hippocampal system represents space to facilitate navigation in one's surroundings. In this Perspective article, I introduce a novel idea that undermines the inherent uniqueness of spatial information in favor of time driving entorhinal-hippocampal activity. Specifically, by spatializing events that occur in succession (i.e., across time), the entorhinal-hippocampal system is critical for all types of cognitive representations. I back up this argument with empirical evidence that hints at a role for the entorhinal-hippocampal system in non-spatial representation, and computational models of the logarithmic compression of time in the brain.

Place cells create landmarks

Hippocampal place cells are known to process path integration signals and sensory information about landmarks. In this issue of Neuron, Fischler-Ruiz et al., 2021 show how localized olfactory cues interact with path integration to drive place cells, turning the cues into landmarks.

Spatial representability of neuronal activity

A common approach to interpreting spiking activity is based on identifying the firing fields—regions in physical or configuration spaces that elicit responses of neurons. Common examples include hippocampal place cells that fire at preferred locations in the navigated environment, head direction cells that fire at preferred orientations of the animal’s head, view cells that respond to preferred spots in the visual field, etc. In all these cases, firing fields were discovered empirically, by trial and error. We argue that the existence and a number of properties of the firing fields can be established theoretically, through topological analyses of the neuronal spiking activity. In particular, we use Leray criterion powered by persistent homology theory, Eckhoff conditions and Region Connection Calculus to verify consistency of neuronal responses with a single coherent representation of space.

Olfactory landmarks and path integration converge to form a cognitive spatial map

The convergence of internal path integration and external sensory landmarks generates a cognitive spatial map in the hippocampus. We studied how localized odor cues are recognized as landmarks by recording the activity of neurons in CA1 during a virtual navigation task. We found that odor cues enriched place cell representations, dramatically improving navigation. Presentation of the same odor at different locations generated distinct place cell representations. An odor cue at a proximal location enhanced the local place cell density and also led to the formation of place cells beyond the cue. This resulted in the recognition of a second, more distal odor cue as a distinct landmark, suggesting an iterative mechanism for extending spatial representations into unknown territory. Our results establish that odors can serve as landmarks, motivating a model in which path integration and odor landmarks interact sequentially and iteratively to generate cognitive spatial maps over long distances.

Crucial role for CA2 inputs in the sequential organization of CA1 time cells supporting memory

There is considerable evidence for hippocampal time cells that briefly activate in succession to represent the temporal structure of memories. Previous studies have shown that time cells can be disrupted while leaving place cells intact, indicating that spatial and temporal information can be coded in parallel. However, the circuits in which spatial and temporal information are coded have not been clearly identified. Here we investigated temporal and spatial coding by dorsal hippocampal CA1 (dCA1) neurons in mice trained on a classic spatial working-memory task. On each trial, the mice approached the same choice point on a maze but were trained to alternate between traversing one of two distinct spatial routes (spatial coding phase). In between trials, there was a 10-s mnemonic delay during which the mouse continuously ran in a fixed location (temporal coding phase). Using cell-type-specific optogenetic methods, we found that inhibiting dorsal CA2 (dCA2) inputs into dCA1 degraded time cell coding during the mnemonic delay and impaired the mouse's subsequent memory-guided choice. Conversely, inhibiting dCA2 inputs during the spatial coding phase had a negligible effect on place cell activity in dCA1 and no effect on behavior. Collectively, our work demonstrates that spatial and temporal coding in dCA1 is largely segregated with respect to the dCA2-dCA1 circuit and suggests that CA2 plays a critical role in representing the flow of time in memory within the hippocampal network.

What are grid-like responses doing in the orbitofrontal cortex?

In 2005, the Moser group identified a new type of cell in the entorhinal cortex (ERC): the grid cell (Hafting, Nature, 436, 2005, pp. 801-806). A landmark series of studies from these investigators showed that grid cells support spatial navigation by encoding position, direction as well as distance information, and they subsequently found grid cells in pre- and para-subiculum areas adjacent to the ERC (Boccara, Nature Neuroscience, 13, 2010, pp. 987-994). Fast forward to 2010, when some clever investigators developed fMRI analysis methods to document grid-like responses in the human ERC (Doeller, Nature, 463, 2010, pp. 657-661). What was not at all expected was the co-identification of grid-like fMRI responses outside of the ERC, in particular, the orbitofrontal cortex (OFC) and the ventromedial prefrontal cortex (vmPFC). Here we provide a compact overview of the burgeoning literature on grid cells in both rodent and human species, while considering the intriguing question: what are grid-like responses doing in the OFC and vmPFC? (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

Neural networks trained using predictive models generate representations that recover the underlying low-dimensional latent structure in the data. Here, the authors demonstrate that a network trained on a spatial navigation task generates place-related neural activations similar to those observed in the hippocampus and show that these are related to the latent structure.

Decreased investigatory head scanning during exploration in learning-impaired, aged rats

"Head scanning" is an investigatory behavior that has been linked to spatial exploration and the one-trial formation or strengthening of place cells in the hippocampus. Previous studies have demonstrated that a subset of aged rats with normal spatial learning performance show head scanning rates during a novel, local-global cue-mismatch manipulation that are similar to those of young rats. However, these aged rats demonstrated different patterns of expression of neural activity markers in brain regions associated with spatial learning, perhaps suggesting neural mechanisms that compensate for age-related brain changes. These prior studies did not investigate the head scanning properties of aged rats that had spatial learning impairments. The present study analyzed head scanning behavior in young, aged-unimpaired, and aged-impaired Long Evans rats. Aged-impaired rats performed the head scan behavior at a lower rate than the young rats. These results suggest that decreased attention to spatial landmarks may be a contributing factor to the spatial learning deficits shown by the aged-impaired rats.

A Critical Review of Spatial Abilities in Down and Williams Syndromes: Not All Space Is Created Equal

Down syndrome (DS, Trisomy 21) and Williams syndrome (WS) are two neurodevelopmental disorders of genetic origin that are accompanied by mild to moderate intellectual disability but exhibit distinct cognitive profiles. In this review we discuss our recent work characterizing the real-world spatial learning and memory abilities of adult individuals with DS and WS. We used several different paradigms in which participants locomote freely and have access to coherent input from all sensory modalities to investigate their fundamental egocentric (body-centered or viewpoint-dependent) and allocentric (world-centered or viewpoint-independent) spatial abilities. We found unequivocal evidence that most individuals with DS exhibit low-resolution egocentric and allocentric spatial learning and memory abilities similar to typically developing (TD) children in the same mental age range. In contrast, most individuals with DS exhibit impaired high-resolution allocentric spatial learning and facilitated response learning as compared to TD children. In comparison, whereas most individuals with WS also exhibit facilitated response learning, their low-resolution allocentric spatial learning and memory abilities are severely impaired as compared to both TD children and individuals with DS. Together with work from other laboratories using real-world or virtual reality paradigms, these findings indicate that in order to navigate in their environment most individuals with DS may use either egocentric route learning that does not integrate individual landmarks, or a low-resolution allocentric spatial representation that encodes the relationships between different locations (i.e., cognitive mapping). In contrast, since most individuals with WS are unable to build or use a low-resolution allocentric or configural representation of the environment they may use visually and verbally encoded landmarks as beacons to learn routes. Finally, we discuss the main neural structures implicated in these different spatial processes and explain how the relative preservation or impairment of specific brain functions may engender the unique cognitive profiles observed in individuals with these neurodevelopmental disorders.

Path Integration and Cognitive Mapping Capacities in Down and Williams Syndromes

Williams (WS) and Down (DS) syndromes are neurodevelopmental disorders with distinct genetic origins and different spatial memory profiles. In real-world spatial memory tasks, where spatial information derived from all sensory modalities is available, individuals with DS demonstrate low-resolution spatial learning capacities consistent with their mental age, whereas individuals with WS are severely impaired. However, because WS is associated with severe visuo-constructive processing deficits, it is unclear whether their impairment is due to abnormal visual processing or whether it reflects an inability to build a cognitive map. Here, we tested whether blindfolded individuals with WS or DS, and typically developing (TD) children with similar mental ages, could use path integration to perform an egocentric homing task and return to a starting point. We then evaluated whether they could take shortcuts and navigate along never-traveled trajectories between four objects while blindfolded, thus demonstrating the ability to build a cognitive map. In the homing task, 96% of TD children, 84% of participants with DS and 44% of participants with WS were able to use path integration to return to their starting point consistently. In the cognitive mapping task, 64% of TD children and 74% of participants with DS were able to take shortcuts and use never-traveled trajectories, the hallmark of cognitive mapping ability. In contrast, only one of eighteen participants with WS demonstrated the ability to build a cognitive map. These findings are consistent with the view that hippocampus-dependent spatial learning is severely impacted in WS, whereas it is relatively preserved in DS.

HippoBellum: Acute Cerebellar Modulation Alters Hippocampal Dynamics and Function

Here we examine what effects acute manipulation of the cerebellum, a canonically motor structure, can have on the hippocampus, a canonically cognitive structure. In male and female mice, acute perturbation of the cerebellar vermis (lobule 4/5) or simplex produced reliable and specific effects in hippocampal function at cellular, population, and behavioral levels, including evoked local field potentials, increased hippocampal cFos expression, and altered CA1 calcium event rate, amplitudes, and correlated activity. We additionally noted a selective deficit on an object location memory task, which requires objection-location pairing. We therefore combined cerebellar optogenetic stimulation and CA1 calcium imaging with an object-exploration task, and found that cerebellar stimulation reduced the representation of place fields near objects, and prevented a shift in representation to the novel location when an object was moved. Together, these results clearly demonstrate that acute modulation of the cerebellum alters hippocampal function, and further illustrates that the cerebellum can influence cognitive domains.SIGNIFICANCE STATEMENT The cerebellum, a canonically motor-related structure, is being increasingly recognized for its influence on nonmotor functions and structures. The hippocampus is a brain region critical for cognitive functions, such as episodic memory and spatial navigation. To investigate how modulation of the cerebellum may impact the hippocampus, we stimulated two sites of the cerebellar cortex and examined hippocampal function at multiple levels. We found that cerebellar stimulation strongly modulates hippocampal activity, disrupts spatial memory, and alters object-location processing. Therefore, a canonically cognitive brain area, the hippocampus, is sensitive to cerebellar modulation.

Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time

Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of low-footprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the NeuroSwarms control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5-12 Hz) sequence generation. We demonstrate that multi-agent swarming and reward-approach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.

Postnatal development of the entorhinal cortex: A stereological study in macaque monkeys

The entorhinal cortex is the main gateway for interactions between the neocortex and the hippocampus. Distinct regions, layers, and cells of the hippocampal formation exhibit different profiles of structural and molecular maturation during postnatal development. Here, we provide estimates of neuron number, neuronal soma size, and volume of the different layers and subdivisions of the monkey entorhinal cortex (Eo, Er, Elr, Ei, Elc, Ec, Ecl) during postnatal development. We found different developmental changes in neuronal soma size and volume of distinct layers in different subdivisions, but no changes in neuron number. Layers I and II developed early in most subdivisions. Layer III exhibited early maturation in Ec and Ecl, a two-step/early maturation in Ei and a late maturation in Er. Layers V and VI exhibited an early maturation in Ec and Ecl, a two-step and early maturation in Ei, and a late maturation in Er. Neuronal soma size increased transiently at 6 months of age and decreased thereafter to reach adult size, except in Layer II of Ei, and Layers II and III of Ec and Ecl. These findings support the theory that different hippocampal circuits exhibit distinct developmental profiles, which may subserve the emergence of different hippocampus-dependent memory processes. We discuss how the early maturation of the caudal entorhinal cortex may contribute to path integration and basic allocentric spatial processing, whereas the late maturation of the rostral entorhinal cortex may contribute to the increased precision of allocentric spatial representations and the temporal integration of individual items into episodic memories.

Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework

Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate-and thus clarify the relationship between-description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.

Hippocampal Place Cells Encode Local Surface-Texture Boundaries

The cognitive map is often assumed to be a Euclidean map that isometrically represents the real world (i.e., the Euclidean distance between any two locations in the physical world should be preserved on the cognitive map). However, accumulating evidence suggests that environmental boundaries can distort the mental representations of physical space. For example, the distance between two locations can be remembered as longer than the true physical distance if the locations are separated by a boundary. While this overestimation is observed under different experimental conditions, even when the boundary is formed by flat surface cues, its physiological basis is not well understood. We examined the neural representation of flat surface cue boundaries, and of the space segregated by these boundaries, by recording place cell activity from CA1 and CA3 while rats foraged on a circular track or square platforms with inhomogeneous surface textures. About 40% of the place field edges concentrated near the boundaries on the circular track (significantly above the chance level 33%). Similarly, place field edges were more prevalent near boundaries on the platforms than expected by chance. In both one- and two-dimensional environments, the population vectors of place cell activity changed more abruptly with distance between locations that crossed cue boundaries than between locations within a bounded region. These results show that the locations of surface boundaries were evident as enhanced decorrelations of the neural representations of locations to either side of the boundaries. This enhancement might underlie the cognitive phenomenon of overestimation of distances across boundaries.

Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation

Path integration is one of the functions that support the self-localization ability of animals. Path integration outputs position information after an animal’s movement when initial-position and movement information is input. The core region responsible for this function has been identified as the medial entorhinal cortex (MEC), which is part of the hippocampal formation that constitutes the limbic system. However, a more specific core region has not yet been identified. This research aims to clarify the detailed structure at the cell-firing level in the core region responsible for path integration from fragmentarily accumulated experimental and theoretical findings by reviewing 77 papers. This research draws a novel diagram that describes the MEC, the hippocampus, and their surrounding regions by focusing on the MEC’s input/output (I/O) information. The diagram was created by summarizing the results of exhaustively scrutinizing the papers that are relative to the I/O relationship, the connection relationship, and cell position and firing pattern. From additional investigations, we show function information related to path integration, such as I/O information and the relationship between multiple functions. Furthermore, we constructed an algorithmic hypothesis on I/O information and path-integration calculation method from the diagram and the information of functions related to path integration. The algorithmic hypothesis is composed of regions related to path integration, the I/O relations between them, the calculation performed there, and the information representations (cell-firing pattern) in them. Results of examining the hypothesis confirmed that the core region responsible for path integration was either stellate cells in layer II or pyramidal cells in layer III of the MEC.

Mechanisms Underlying the Neural Computation of Head Direction

Many animals use an internal sense of direction to guide their movements through the world. Neurons selective to head direction are thought to support this directional sense and have been found in a diverse range of species, from insects to primates, highlighting their evolutionary importance. Across species, most head-direction networks share four key properties: a unique representation of direction at all times, persistent activity in the absence of movement, integration of angular velocity to update the representation, and the use of directional cues to correct drift. The dynamics of theorized network structures called ring attractors elegantly account for these properties, but their relationship to brain circuits is unclear. Here, we review experiments in rodents and flies that offer insights into potential neural implementations of ring attractor networks. We suggest that a theory-guided search across model systems for biological mechanisms that enable such dynamics would uncover general principles underlying head-direction circuit function.

Egocentric and allocentric representations of space in the rodent brain

Spatial signals are prevalent within the hippocampus and its neighboring regions. It is generally accepted that these signals are defined with respect to the external world (i.e., a world-centered, or allocentric, frame of reference). Recently, evidence of egocentric processing (i.e., self-centered, defined relative to the subject) in the extended hippocampal system has accumulated. These results support the idea that egocentric sensory information, derived from primary sensory cortical areas, may be transformed to allocentric representations that interact with the allocentric hippocampal system. We propose a framework to explain the implications of the egocentric-allocentric transformations to the functions of the medial temporal lobe memory system.

A new perspective on the head direction cell system and spatial behavior

The head direction cell system is an interconnected set of brain structures containing neurons whose firing is directionally tuned. The robust representation of allocentric direction by head direction cells suggests that they provide a neural compass for the animal. However, evidence linking head direction cells and spatial behavior has been mixed. Whereas damage to the hippocampus yields profound deficits in a range of spatial tasks, lesions to the head direction cell system often yield milder impairments in spatial behavior. In addition, correlational approaches have shown a correspondence between head direction cells and spatial behavior in some tasks, but not others. These mixed effects may be explained in part by a new view of the head direction cell system arising from recent demonstrations of at least two types of head direction cells: 'traditional' cells, and a second class of 'sensory' cells driven by polarising features of an environment. The recognition of different kinds of head direction cells now allows a nuanced assessment of this system's role in guiding navigation.

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Path integration is a navigational strategy that gives an animal an estimate of its position relative to some starting point. For many decades, ingenious and probing behavioural experiments have been the only window onto the operation of path integration in arthropods. New methods have now made it possible to visualise the activity of neural circuits in Drosophila while they fly or walk in virtual reality. Studies of this kind, as well as electrophysiological recordings from single neurons in the brains of other insects, are revealing details of the neural mechanisms that control an insect's direction of travel and other aspects of path integration. The aim here is first to review the major features of path integration in foraging desert ants and honeybees, the current champion path integrators of the insect world, and second consider how the elaborate behaviour of these insects might be accommodated within the framework of the newly understood neural circuits. The discussion focuses particularly on the ability of ants and honeybees to use a celestial compass to give direction in Earth-based coordinates, and of honeybees to use a landscape panorama to provide directional guidance for path integration. The possibility is raised that well-ordered behaviour might in some cases substitute for complex circuitry.