Making a stronger case for comparative research to investigate the behavioral and neurological bases of three-dimensional navigation

The rich diversity of avian natural history provides exciting possibilities for comparative research aimed at understanding three-dimensional navigation. We propose some hypotheses relating differences in natural history to potential behavioral and neurological adaptations possessed by contrasting bird species. This comparative approach may offer unique insights into some of the important questions raised by Jeffery et al

From cognitive maps to spatial schemas

A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas

Spatial subgoal learning in the mouse: behavioral and computational mechanisms

Here we aim to better understand how animals navigate structured environments. The prevailing wisdom is that they can select among two distinct approaches: querying a mental map of the environment or repeating previously successful trajectories to a goal. However, this dichotomy has been built around data from rodents trained to solve mazes, and it is unclear how it applies to more naturalistic scenarios such as self-motivated navigation in open environments with obstacles. In this project, we leveraged instinctive escape behavior in mice to investigate how rodents use a period of exploration to learn about goals and obstacles in an unfamiliar environment. In our most basic assay, mice explore an environment with a shelter and an obstacle for 5-20 minutes and then we present threat stimuli to trigger escapes to shelter. After 5-10 minutes of exploration, mice took inefficient paths to the shelter, often nearly running into the obstacle and then relying on visual and tactile cues to avoid it. Within twenty minutes, however, they spontaneously developed an efficient subgoal strategy, escaping directly to the obstacle edge before heading to the shelter. Mice escaped in this manner even if the obstacle was removed, suggesting that they had memorized a mental map of subgoals. Unlike typical models of map-based planning, however, we found that investigating the obstacle was not important for updating the map. Instead, learning resembled trajectory repetition: mice had to execute `practice runs' toward an obstacle edge in order to memorize subgoals. To test this hypothesis directly, we developed a closed-loop neural manipulation, interrupting spontaneous practice runs by stimulating premotor cortex. This manipulation successfully prevented subgoal learning, whereas several control manipulations did not. We modelled these results using a panel of reinforcement learning approaches and found that mice behavior is best matched by systems that explore in a non-uniform manner and possess a high-level spatial representation of regions in the arena. We conclude that mice use practice runs to learn useful subgoals and integrate them into a hierarchical cognitive map of their surroundings. These results broaden our understanding of the cognitive toolkit that mammals use to acquire spatial knowledge

Spatial representation for navigation in animats

This article considers the problem of spatial representation for animat navigation systems. It is proposed that the global navigation task, or "wayfinding, " is best supported by multiple interacting subsystems, each of which builds its own partial representation of relevant world knowledge. Evidence from the study of animal navigation is reviewed to demonstrate that similar principles underlie the wayfinding behavior of animals, including humans. A simulated wayfinding system is described that embodies and illustrates several of the themes identified with animat navigation. This system constructs a network of partial models of the quantitative spatial relations between groups of salient landmarks. Navigation tasks are solved by propagating egocentric view information through this network, using a simple but effective heuristic to arbitrate between multiple solutions

Theoretical Computational Models for the Cognitive Map

In den letzten Jahrzehnten hat die Forschung nach der Frage, wie Raum im Gehirn repräsentiert wird, ein weit verzweigtes Netzwerk von spezialisierten Zellen aufgedeckt. Es ist nun klar, dass Räumlichkeit auf irgendeine Art repräsentiert sein muss, aber die genaue Umsetzung wird nach wie vor debattiert. Folgerichtig liegt das übergeordnete Ziel meiner Dissertation darin, das Verständnis von der neuronalen Repräsentation, der Kognitiven Karte, mithilfe von theoretischer Computermodellierung (im Gegensatz zu datengetriebener Modellierung) zu erweitern. Die Arbeit setzt sich aus vier Publikationen zusammen, die das Problem aus verschiedenen, aber miteinander kompatiblen Richtungen angehen: In den ersten beiden Publikationen geht es um zielgerichtete Navigation durch topologische Graphen, in denen die erkundete Umgebung als Netzwerk aus loka len Positionen und sie verbindenden Handlungen dargestellt wird. Im Gegensatz zu Koordinaten-basierten metrischen Karten sind Graphenmodelle weniger gebunden und haben verschiedene Vorteile wie z.B. Algorithmen, die garantiert optimale Pfade finden. Im ersten Modell sind Orte durch Populationen von einfachen Bildfeatures im Graphen gespeichert. Für die Navigation werden dann mehrere Pfade gleichzeitig zwischen Start- und Zielpopulationen berechnet und die schlussendliche Route folgt dem Durchschnitt der Pfade. Diese Methode macht die Wegsuche robuster und umgeht das Problem, Orte entlang der Route wiedererkennen zu müssen. In der zweiten Publikation wird ein hierarchisches Graphenmodell vorgeschlagen, bei dem die Umgebung in mehrere Regionen unterteilt ist. Das Regionenwissen ist ebenfalls als übergeordnete Knoten im Graphen gespeichert. Diese Struktur führt bei der Wegsuche dazu, dass die berechneten Routen verzerrt sind, was mit dem Verhalten von menschlichen Probanden in Navigationsstudien übereinstimmt. In der dritten Publikation geht es auch um Regionen, der Fokus liegt aber auf der konkreten biologischen Umsetzung in Form von Place Cell und Grid Cell-Aktivität. Im Gegensatz zu einzigartigen Ortsknoten im Graphen zeigen Place Cells multiple Feuerfelder in verschiedenen Regionen oder Kontexten. Dieses Phänomen wird als Remapping bezeichnet und könnte der Mechanismus hinter Regionenwissen sein. Wir modellieren das Phänomen mithilfe eines Attraktor-Netzwerks aus Place- und Grid Cells: Immer, wenn sich der virtuelle Agent des Modells von einer Region in eine andere bewegt, verändert sich der Kontext und die Zellaktivität springt zu einem anderen Attraktor, was zu einem Remapping führt. Das Modell kann die Zellaktivität von Tieren in mehreren Experimentalumgebungen replizieren und ist daher eine plausible Erklärung für die Vorgänge im biologischen Gehirn. In der vierten Publikation geht es um den Vergleich von Graphen- und Kartenmodellen als fundamentale Struktur der kognitiven Karte. Im Speziellen geht es bei dieser Debatte um die Unterscheidung zwischen nicht-metrischen Graphen und metrischen euklidischen Karten; euklidische Karten sind zwar mächtiger als die Alternative, aber menschliche Probanden neigen dazu, Fehler zu machen, die stark von einer metrischen Vorhersage abweichen. Deshalb wird häufig argumentiert, dass nicht-metrische Modelle das Verhalten besser erklären können. Wir schlagen eine alternative metrische Erklärung für die nichtmetrischen Graphen vor, indem wir die Graphen im metrischen Raum einbetten. Die Methode wird in einer bestimmten nicht-euklidischen Beispielumgebung gezeigt, in der sie Versuchspersonenverhalten genauso gut vorhersagen kann, wie ein nichtmetrischer Graph. Wir argumentieren daher, dass unser Modell ein besseres Modell für Raumrepräsentation sein könnte. Zusätzlich zu den Einzelergebnissen diskutiere ich außerdem die Gemeinsamkeiten der Modelle und wie sie in den derzeitigen Stand der Forschung zur kognitiven Karte passen. Darüber hinaus erörtere ich, wie die Ergebnisse zu komplexeren Modellen vereint werden könnten, um unser Bild der Raumkognition zu erweitern.Decades of research into the neural representation of physical space have uncovered a complex and distributed network of specialized cells in the mammalian brain. It is now clear that space is represented in some form, but the realization remains debated. Accordingly, the overall aim of my thesis is to further the understanding of the neural representation of space, the cognitive map, with the aid of theoretical computational modeling (as opposed to data-driven modeling). It consists of four separate publications which approach the problem from different but complementing perspectives: The first two publications consider goal-directed navigation with topological graph models, which encode the environment as a state-action graph of local positions connected by simple movement instructions. Graph models are often less constrained than coordinate-based metric maps and offer a variety of computational advantages; for example, graph search algorithms may be used to derive optimal routes between arbitrary positions. In the first model, places are encoded by population codes of low-level image features. For goal-directed navigation, a set of simultaneous paths is obtained between the start and goal populations and the final trajectory follows the population average. This makes route following more robust and circumvents problems related to place recognition. The second model proposes a hierarchical place graph which subdivides the known environment into well-defined regions. The region knowledge is included in the graph as superordinate nodes. During wayfinding, these nodes distort the resulting paths in a way that matches region-related biases observed in human navigation experiments. The third publication also considers region coding but focuses on more concrete biological implementation in the form of place cell and grid cell activity. As opposed to unique nodes in a graph, place cells may express multiple firing fields in different contexts or regions. This phenomenon is known as “remapping” and may be fundamental to the encoding region knowledge. The dynamics are modeled in a joint attractor neural network of place and grid cells: Whenever a virtual agent moves into another region, the context changes and the model remaps the cell activity to an associated pattern from memory. The model is able to replicate experimental findings in a series of mazes and may therefore be an explanation for the observed activity in the biological brain. The fourth publication again returns to graph models, joining the debate on the fundamental structure of the cognitive map: The internal representation of space has often been argued to either take the form of a non-metric topological graph or a Euclidean metric map in which places are assigned specific coordinates. While the Euclidean map is more powerful, human navigation in experiments often strongly deviates from a (correct) metric prediction, which has been taken as an argument for the non-metric alternative. However, it may also be possible to find an alternative metric explanation to the non-metric graphs by embedding the latter into metric space. The method is shown with a specific non-Euclidean example environment where it can explain subject behavior equally well to the purely non-metric graph, and it is argued that it is therefore a better model for spatial knowledge. Beyond the individual results, the thesis discusses the commonalities of the models and how they compare to current research on the cognitive map. I also consider how the findings may be combined into more complex models to further the understanding of the cognitive neuroscience of space

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates