Cognitive maps

…questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Animals learn complex cognitive maps to navigate through physical environments or reason about abstract concepts. These maps are believed to be encoded by mammalian hippocampal grid and place cells, forming low-dimensional neural manifolds that support flexible navigation. However, beyond simple line and ring attractors, how the brain represents and navigates more complex neural manifolds remains unclear. In this study, we propose a solution for representing arbitrary nonlinear neural manifolds by approximating them with piecewise linear spaces composed of simplexes, known as “simplicial compl

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. The hippocampus, a key brain region for these maps, exhibits neural population activity that often evolves along nonlinear, low-dimensional manifolds. While cognitive maps have been characterized at the level of cell tunings to spatial location and decorrelations over time, a precise, unsupervised, and quantitative model of how this map forms and morphs at the population level remains elusive due to the large dimensionality and nonlinearity of neural activity. Current dimensionality reduction t

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. We employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. The results provide a de

…distinctions between schemas and cognitive maps, and how these processes change from childhood to adulthood, alongside differences in how researchers define the underlying concepts.

Cognitive maps have been proposed as an essential relational structure for human memory and thought (Tolman, 1948; Constantinescu et al., 2016). In this format, remembered entities are embedded in space and their relationships are encoded via concrete angles and Euclidean distances. Although much of higher human cognition may be spatial, cognitive maps are not the only possible relational structure. With a behavioral experiment we aim to challenge the extent of the cognitive map hypothesis and to investigate when and how humans represent knowledge via the relational structure of cognitive grap

Cognitive maps are abstract internal representations of the external world that facilitate planning and efficient navigation. How the brain learns such maps from variable sensory experience remains elusive. Previous models either relied on discrete environments [1], linear embeddings of continuous states [2], or generative models trained by predicting sensory input [3]. However, the world is neither discrete nor linear, and the brain presumably does not learn a generative model of its sensory inputs. So, how can the brain learn a map and use it to plan strategically? In this work, we develop

…the environment, forming personalized cognitive maps. The hippocampal area CA1 features spatial cells that adapt based on behavior and internal states. Computational models have usually obtained spatial tuning by training a deep recurrent network for solving path integration, over numerous epochs, using backpropagation [1, 2, 3]. However, such methods do not closely align with the real-time local learning used by animals. Additionally, the formed spatial maps are solely oriented towards solving a specific task, and fail to c

Aims: Decision making relies on cognitive maps of available options and expected outcomes. The dopaminergic (DA) system can encode such expectations, and discrepancies between expected and actual outcomes, the so-called Reward Prediction Error (RPE). Here we addressed the link between decision strategies, contingency representation and RPE.Methods: In a circular arena with three locations associated with rewards, mice are conditioned to perform sequences of binary choices. To promote shifts in decision strategy and the associated neural representation of actions, we used exactly the same setti

The hippocampus is the episodic memory and spatial navigation center of the brain. The cognitive map metaphor identifies episodic memory with ``navigation'' through a space of arbitrary learned relationships. However, what structures are admissible as ``cognitive maps'' remains unclear, particularly whether these maps are constrained to obey the same rules as physical space. We hypothesize that the hippocampus learns metric spaces as its cognitive maps. We test this hypothesis by training mice to learn and solve relational queries in a finite metric space which cannot be embedded in 2D Euclide

Since its initial conceptualization by Edward Tolman, cognitive maps – i.e., task-derived internal representations of an environment – have been found in multiple brain regions. In mammalian brains, the orbitofrontal cortex (OFC) can produce a flexible cognitive map underpinning model-based learning. Recent results have shown that this function is compatible with the canonical value-encoding role ascribed to OFC, whereby this region guides decision-making by providing a representation of reward. Hence, recent theories have suggested that the OFC integrates activity from other brain regions and

The brain's extraordinary abilities are often associated with its ability to learn and adapt. But memory and plasticity, in general, have their limitations, especially when faced with the tasks such as retrieving hundreds of thousands of items, such as in the case of scatter-hoarding animals. Here, we present a brain mechanism that works by utilizing cryptographic principles in lieu of plasticity. Rather than memorizing the locations of their caches, as previously suggested, we propose that cache-hoarding animals use a single cryptographic-like mechanism for both caching and retrieval. The mod

Cognitive maps associate complex, high-dimensional stimuli, such as visual percepts, with abstract latent variables, such as spatial locations. It has been argued that animals use these abstract spatial features to navigate and infer novel shortcuts in their environments. Employing information theory, we present a simple computational account of how such abstract spatial representations may emerge from interactions with high-dimensional stimuli: We show that learning world models in which the dynamics are parsimonious and apply (approximately) independently of the state in an abstract state sp

Cognitive maps are hypothesized memory systems that organize sensory experience into knowledge that can be used flexibly in novel scenarios, such as finding new routes during navigation. Evidence for this hypothesis has been largely limited to experiments with fully observable sensory information. However, to serve as a genuine internal source of knowledge, cognitive maps should also be able to support mental computations in the absence of external cues. To test this hypothesis, we developed a mental navigation task in which monkeys must produce vectors that correspond to moving between pairs

The brain forms cognitive maps of relational knowledge, an organizing principle thought to underlie our ability to generalize and make inferences. Such map-like representations may facilitate goal-directed behavior by enabling generalization of information across related states. However, it is not fully understood what neural and computational mechanisms give rise to map-based generalization, nor how a relevant map may be selected when a stimulus is embedded in multiple relational structures. Here, we find that both spatial and temporal cognitive maps influence reward generalization in a choic