Map-like Representations Research Articles

Distinct codes for environment structure and symmetry in postrhinal and retrosplenial cortices

Complex sensory information arrives in the brain from an animal’s first-person (‘egocentric’) perspective. However, animals can efficiently navigate as if referencing map-like (‘allocentric’) representations. The postrhinal (POR) and retrosplenial (RSC) cortices are thought to mediate between sensory input and internal maps, combining egocentric representations of physical cues with allocentric head direction (HD) information. Here we show that neurons in the POR and RSC of female Long-Evans rats are tuned to distinct but complementary aspects of local space. Egocentric bearing (EB) cells recorded in square and L-shaped environments reveal that RSC cells encode local geometric features, while POR cells encode a more global account of boundary geometry. Additionally, POR HD cells can incorporate egocentric information to fire in two opposite directions with two oppositely placed identical visual landmarks, while only a subset of RSC HD cells possess this property. Entorhinal grid and HD cells exhibit consistently allocentric spatial firing properties. These results reveal significant regional differences in the neural encoding of spatial reference frames.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Holdout based blending approaches for improved satellite image classification

An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.

Dissociable contributions of the hippocampus and orbitofrontal cortex to representing task space in a social context.

The hippocampus (HC) and the orbitofrontal cortex (OFC) jointly encode a map-like representation of a task space to guide behavior. It remains unclear how the OFC and HC interact in encoding this map-like representation, though previous studies indicated that both regions have different functions. We acquired the functional magnetic resonance imaging data under a social navigation task in which participants interacted with characters in a two-dimensional "social space." We calculate the social relationships between the participants and characters and used a drift-diffusion model to capture the inner process of social interaction. Then we used multivoxel pattern analysis to explore the brain-behavior relationship. We found that (i) both the HC and the OFC showed higher activations during the selective trial than the narrative trial; (ii) the neural pattern of the right HC was associated with evidence accumulation during social interaction, and the pattern of the right lateral OFC was associated with the social relationship; (iii) the neural pattern of the HC can decode the participants choices, while the neural pattern of the OFC can decode the task information about trials. The study provided evidence for distinct roles of the HC and the OFC in encoding different information when representing social space.

Volitional activation of remote place representations with a hippocampal brain-machine interface.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex

Humans can navigate flexibly to meet their goals. Here, we asked how the neural representation of allocentric space is distorted by goal-directed behavior. Participants navigated an agent to two successive goal locations in a grid world environment comprising four interlinked rooms, with a contextual cue indicating the conditional dependence of one goal location on another. Examining the neural geometry by which room and context were encoded in fMRI signals, we found that map-like representations of the environment emerged in both hippocampus and neocortex. Cognitive maps in hippocampus and orbitofrontal cortices were compressed so that locations cued as goals were coded together in neural state space, and these distortions predicted successful learning. This effect was captured by a computational model in which current and prospective locations are jointly encoded in a place code, providing a theory of how goals warp the neural representation of space in macroscopic neural signals.

Do Advanced Spatial Strategies Depend on the Number of Flight Hours? The Case of Military Pilots.

Background: Military pilots show advanced visuospatial skills. Previous studies demonstrate that they are better at mentally rotating a target, taking different perspectives, estimating distances and planning travel and have a topographic memory. Here, we compared navigational cognitive styles between military pilots and people without flight experience. Pilots were expected to be more survey-style users than nonpilots, showing more advanced navigational strategies. Method: A total of 106 military jet pilots from the Italian Air Force and 92 nonpilots from the general population matched for education with the pilots were enrolled to investigate group differences in navigational styles. The participants were asked to perform a reduced version of the Spatial Cognitive Style Test (SCST), consisting of six tasks that allow us to distinguish individuals in terms of landmark (people orient themselves by using a figurative memory for environmental objects), route (people use an egocentric representation of the space) and survey (people have a map-like representation of the space) user styles. Results: In line with our hypothesis, military pilots mainly adopt the survey style, whereas nonpilots mainly adopt the route style. In addition, pilots outperformed nonpilots in both the 3D Rotation Task and Map Description Task. Conclusions: Military flight expertise influences some aspects of spatial ability, leading to enhanced human navigation. However, it must be considered that they are a population whose navigational skills were already high at the time of selection at the academy before formal training began.

Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity.

The ventral visual stream of the human brain is subdivided into patches with categorical stimulus preferences, like faces or scenes. However, the functional organization within these areas is less clear. Here, we used functional magnetic resonance imaging and vertex-wise tuning models to independently probe spatial and face-part preferences in the inferior occipital gyrus (IOG) of healthy adult males and females. The majority of responses were well explained by Gaussian population tuning curves for both retinotopic location and the preferred relative position within a face. Parameter maps revealed a common gradient of spatial and face-part selectivity, with the width of tuning curves drastically increasing from posterior to anterior IOG. Tuning peaks clustered more idiosyncratically but were also correlated across maps of visual and face space. Preferences for the upper visual field went along with significantly increased coverage of the upper half of the face, matching recently discovered biases in human perception. Our findings reveal a broad range of neural face-part selectivity in IOG, ranging from narrow to “holistic.” IOG is functionally organized along this gradient, which in turn is correlated with retinotopy.SIGNIFICANCE STATEMENT Brain imaging has revealed a lot about the large-scale organization of the human brain and visual system. For example, occipital cortex contains map-like representations of the visual field, while neurons in ventral areas cluster into patches with categorical preferences, like faces or scenes. Much less is known about the functional organization within these areas. Here, we focused on a well established face-preferring area—the inferior occipital gyrus (IOG). A novel neuroimaging paradigm allowed us to map the retinotopic and face-part tuning of many recording sites in IOG independently. We found a steep posterior–anterior gradient of decreasing face-part selectivity, which correlated with retinotopy. This suggests the functional role of ventral areas is not uniform and may follow retinotopic “protomaps.”

Avian forebrain processing of magnetic intensity and inclination: hippocampus, anterior forebrain Wulst and an unexpected double-dissociation

It is often neglected that the ability of birds to extract navigational information from the earth’s magnetic field can be shaped by learning and memory, which would necessarily recruit brain regions of the telencephalon that support cognitive processes. In the current study, we exploited a validated, conditioning experimental paradigm to explore the possible role of the homing pigeon hippocampal formation (HF) and anterior forebrain Wulst in the detection of variation in both magnetic field intensity and inclination. Whereas HF lesions resulted in a complete loss of intensity discrimination while sparing inclination discrimination, Wulst lesions had the opposite effect, resulting in a complete loss of inclination discrimination while sparing intensity discrimination. It is not surprising that Wulst lesions should disrupt inclination discrimination because in migratory songbirds, regions of the Wulst are known to support the geomagnetic compass, which relies on inclination. More challenging is explaining why HF should be sensitive to variation in magnetic intensity. We suggest that the observed HF sensitivity to intensity may reflect incorporation of geomagnetic irregularities (anomalies) as pigeons learn a map-like representation of familiar landmarks and landscape features. The parallel processing of magnetic intensity and inclination in the homing pigeon forebrain sets the foundation for future studies designed to better understand how cognitive processing can influence geomagnetically guided navigational behavior in birds.

Map-Like Representations of an Abstract Conceptual Space in the Human Brain.

Map-Like Representations of an Abstract Conceptual Space in the Human Brain.

Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals

Neural circuits generate representations of the external world from multiple information streams. The navigation system provides an exceptional lens through which we may gain insights about how such computations are implemented. Neural circuits in the medial temporal lobe construct a map-like representation of space that supports navigation. This computation integrates multiple sensory cues, and, in addition, is thought to require cues related to the individual’s movement through the environment. Here, we identify multiple self-motion signals, related to the position and velocity of the head and eyes, encoded by neurons in a key node of the navigation circuitry of mice, the medial entorhinal cortex (MEC). The representation of these signals is highly integrated with other cues in individual neurons. Such information could be used to compute the allocentric location of landmarks from visual cues and to generate internal representations of space.

The Hippocampus Maps Concept Space, Not Feature Space.

The hippocampal formation encodes maps of space and a key question in neuroscience is whether its spatial coding principles also provide a universal metric for the organization of nonspatial, conceptual information. Previous work demonstrated directional coding during navigation through a continuous stimulus feature space as well as mapping of distances in a feature space that was relevant for concept learning. Here we provide the first unambiguous evidence for a hippocampal representation of the actual concept space, by showing that the hippocampal distance signal selectively reflects the mapping of specifically conceptually relevant rather than of all feature dimensions. During fMRI scanning of 32 human participants (21 females), we presented everyday objects, which had beforehand been associated with specific values on three continuous feature dimensions. Crucially, only two dimensions were relevant to prior concept learning. We find that hippocampal responses to the objects reflect their relative distances in a space defined along conceptually relevant dimensions compared with distances in a space defined along all feature dimensions. These findings suggest that the hippocampus supports knowledge acquisition by dynamically encoding information in a space spanned along dimensions that are relevant in relation to define concepts.SIGNIFICANCE STATEMENT How are neural representations of conceptual knowledge organized, such that humans are able to infer never experienced relations or categorize new exemplars? Map-like representations as supported by the hippocampal formation to encode physical space during navigation have been suggested as a suitable format. Here we provide the first evidence for a hippocampal representation of a conceptual space compared with a general feature-based space.

Medial Prefrontal Cortex Represents the Object-Based Cognitive Map When Remembering an Egocentric Target Location.

A cognitive map, representing an environment around oneself, is necessary for spatial navigation. However, compared with its constituent elements such as individual landmarks, neural substrates of coherent spatial information, which consists in a relationship among the individual elements, remain largely unknown. The present study investigated how the brain codes map-like representations in a virtual environment specified by the relative positions of three objects. Representational similarity analysis revealed an object-based spatial representation in the hippocampus (HPC) when participants located themselves within the environment, while the medial prefrontal cortex (mPFC) represented it when they recollected a target object's location relative to their self-body. During recollection, task-dependent functional connectivity increased between the two areas implying exchange of self-location and target location signals between the HPC and mPFC. Together, the object-based cognitive map, whose coherent spatial information could be formed by objects, may be recruited in the HPC and mPFC for complementary functions during navigation, which may generalize to other aspects of cognition, such as navigating social interactions.

Foraging in Mind

People and other animals can search for information inside their heads. Where does this ability come from, and what does it enable cognitive systems to do? In this article, we address the behavioral and cognitive similarities between search in external environments and internal environments (e.g., memory). These require both maplike representations and the means to navigate them, and the latter involves modulation between exploitation and exploration analogous to a foraging process called area-restricted search. These findings have implications for understanding a number of cognitive abilities commonly considered to be hallmarks of the human species, such as well-developed executive control and goal-directed cognition, autonoetic consciousness (i.e., self-awareness), deliberation, and free will. Moreover, this research extends our conception of what organisms may share these abilities and how they evolved.

Concepts and Compositionality: In Search of the Brain's Language of Thought.

Imagine Genghis Khan, Aretha Franklin, and the Cleveland Cavaliers performing an opera on Maui. This silly sentence makes a serious point: As humans, we can flexibly generate and comprehend an unbounded number of complex ideas. Little is known, however, about how our brains accomplish this. Here we assemble clues from disparate areas of cognitive neuroscience, integrating recent research on language, memory, episodic simulation, and computational models of high-level cognition. Our review is framed by Fodor's classic language of thought hypothesis, according to which our minds employ an amodal, language-like system for combining and recombining simple concepts to form more complex thoughts. Here, we highlight emerging work on combinatorial processes in the brain and consider this work's relation to the language of thought. We review evidence for distinct, but complementary, contributions of map-like representations in subregions of the default mode network and sentence-like representations of conceptual relations in regions of the temporal and prefrontal cortex.

Rapid learning of a spatial memory task in a lacertid lizard (Podarcis liolepis)

Mammals and birds are capable of navigating to a goal using learned map-like representations of space (i.e. place learning), but research assessing this navigational strategy in reptiles has produced inconclusive results, in part due to the use of procedures that do not take account of the peculiarities of reptilian behavior and physiology. Here I present a procedure suitable for testing spatial cognition that exploits a naturally evolved, ethologically relevant ability common to many lizards (i.e. refuge seeking behavior). The procedure requires lizards to learn the location of an open refuge inside a rectangular arena containing artificial refuges in every corner, using distal extramaze visual cues and with no local cues marking the location of the open refuge. The procedure probes the lizards’ place learning ability and effectively rules out the use of egocentric and response-based strategies. The described procedure was successfully used to demonstrate place learning in a lacertid lizard (Podarcis liolepis). Over the course of two weeks of training both the latency to entering the open refuge and the number of corners visited in each trial decreased gradually, indicating that learning had taken place in over 60% of the lizards tested. These results confirm that, under certain circumstances, lizards are capable of navigating to a goal using a place learning strategy.

Does spatial cognitive style affect how navigational strategy is planned?

People orient themselves in the environment using three different, hierarchically organized, spatial cognitive styles: landmark, route, and survey. Landmark style is based on a representation encompassing only visual information (terrain features); route style is based on a representation that connects landmarks and routes using an egocentric (body-centred) frame of reference; survey style is based on a global map-like representation that mainly involves an allocentric (world-centred) frame of reference. This study was aimed at investigating whether individual spatial cognitive style affected the way to plan a path when searching for a lost object. Participants with landmark, route, and survey style were assessed with an ecological navigational planning task (the Key Search Task), which required planning a strategy to search for the lost key in a hypothetical wide squared field. Results showed that spatial cognitive styles were associated to different navigational planning strategies, although the time to complete the Key Search Task was comparable across the styles. As revealed by the Key Search Task score, survey style individuals were the best navigational planners, route style individuals were less efficient and landmark style individuals were the least efficient. These results suggest that spatial cognitive style has effects on navigational planning. Implications for clinical settings, such as for developmental topographical disorientation, are discussed.

TRANSLATING AERIAL IMAGES INTO STREET-MAP-LIKE REPRESENTATIONS FOR VISUAL SELF-LOCALIZATION OF UAVS

Abstract. Unmanned aerial vehicles (UAVs) rely on global navigation satellite systems (GNSS) like the Global Positioning System (GPS) for navigation but GNSS signals can be easily jammed. Therefore, we propose a visual localization method that uses a camera and data from Open Street Maps in order to replace GNSS. First, the aerial imagery from the onboard camera is translated into a map-like representation. Then we match it with a reference map to infer the vehicle’s position. An experiment over a typical sized mission area shows localization accuracy close to commercial GPS. Compared to previous methods ours is applicable to a broader range of scenarios. It can incorporate multiple types of landmarks like roads and buildings and it outputs absolute positions with higher frequency and confidence and can be used at altitudes typical for commercial UAVs. Our results show that the proposed method can serve as a backup to GNSS systems where suitable landmarks are available.

The Hippocampus Encodes Distances in Multidimensional Feature Space

The hippocampal formation encodes maps of the physical environment [1-5]. A key question in neuroscience is whether its spatial coding principles also provide a universal metric for the organization of non-spatial information. Initial evidence comes from studies revealing directional modulation of fMRI responses in humans [6, 7] during navigation through abstract spaces and the involvement of place and grid cells in encoding of non-spatial feature dimensions [8]. However, a critical feature of a map-like representation is information about distances between locations, which has yet only been demonstrated for physical space [4, 9]. Here, we probe whether the hippocampus similarly encodes distances between points in an abstract space spanned by continuous stimulus-feature dimensions that were relevant to the acquisition of a novel concept. We find that, after learning, two-dimensional distances between individual positions in the abstract space were represented in the hippocampal multivoxel pattern as well as in the univariate hippocampal signal as indexed by fMRI adaptation. These results support the notion that the hippocampus computes domain-general, multidimensional cognitive maps along continuous dimensions.

Navigation behavior based on self-organized spatial representation in hierarchical recurrent neural network

ABSTRACTA cognitive map is an internal model of the external world and contains the spatial representation of the surrounding environment. The existence of the cognitive map was first identified in rats; rats can navigate to their desired destination using cognitive maps while dealing with environmental uncertainty. We performed a mobile robot navigation experiment where obstacles were randomly placed using hierarchical recurrent neural network (HRNN) with multiple timescales. The HRNN was trained to navigate the mobile robot to the destination indicated by a snapshot image. After the training, the HRNN was able to successfully avoid the obstacles and navigate to the destination from any location in the environment. Analysis of the internal states of the HRNN showed that the module with fast timescale handles obstacle avoidance and the one with slow timescale has spatial representation corresponding to the spatial position of the destination. Moreover, in the experiment wherein the novel path appeared, the trained HRNN performed shortcut behavior. The shortcut behavior shows that the HRNN performed navigation using the self-organized spatial representation in the slow recurrent neural network. This indicates that training of goal-oriented navigation, i.e. the navigation motivated by a snapshot image of the destination results in the self-organization of cognitive map-like representation.