How to build a cognitive map

Functional specialization of medial and lateral orbitofrontal cortex in inferential decision-making

Inferring prospective outcomes and updating behavior are prerequisites for making flexible decisions in the changing world. These abilities are highly associated with the functions of the orbitofrontal cortex (OFC) in humans and animals. The functional specialization of OFC subregions in decision-making has been established in animals. However, the understanding of how human OFC contributes to decision-making remains limited. Therefore, we studied this issue by examining the information representation and functional interactions of human OFC subregions during inference-based decision-making. We found that the medial OFC (mOFC) and lateral OFC (lOFC) collectively represented the inferred outcomes which, however, were context-general coding in the mOFC and context-specific in the lOFC. Furthermore, the mOFC-motor and lOFC-frontoparietal functional connectivity may indicate the motor execution of mOFC and the cognitive control of lOFC during behavioral updating. In conclusion, our findings support the dissociable functional roles of OFC subregions in decision-making.

Modeling the grid cell activity based on cognitive space transformation

The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.

Remapping revisited: how the hippocampus represents different spaces

The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.

Cognitive maps and the magnetic sense in vertebrates

Navigation requires a network of neurons processing inputs from internally generated cues and external landmarks. Most studies on the neuronal basis of navigation in vertebrates have focused on rats and mice and the canonical senses vision, hearing, olfaction, and somatosensation. Some animals have evolved the ability to sense the Earth's magnetic field and use it for orientation. It can be expected that in these animals magnetic cues are integrated with other sensory cues in the cognitive map. We provide an overview of the behavioral evidence and brain regions involved in magnetic sensing in support of this idea, hoping that this will guide future experiments.

Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.

Bayesian reinforcement learning: A basic overview

We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.

Topological structure of population activity in mouse visual cortex encodes densely sampled stimulus rotations

The primary visual cortex is one of the most well understood regions supporting the processing involved in sensory computation. Following the popularization of high-density neural recordings, it has been observed that the activity of large neural populations is often constrained to low dimensional manifolds. In this work, we quantify the structure of such neural manifolds in the visual cortex. We do this by analyzing publicly available two-photon optical recordings of mouse primary visual cortex in response to visual stimuli with a densely sampled rotation angle. Using a geodesic metric along with persistent homology, we discover that population activity in response to such stimuli generates a circular manifold, encoding the angle of rotation. Furthermore, we observe that this circular manifold is expressed differently in subpopulations of neurons with differing orientation and direction selectivity. Finally, we discuss some of the obstacles to reliably retrieving the truthful topology generated by a neural population.

Task-anchored grid cell firing is selectively associated with successful path integration-dependent behaviour

Grid firing fields have been proposed as a neural substrate for spatial localisation in general or for path integration in particular. To distinguish these possibilities, we investigate firing of grid and non-grid cells in the mouse medial entorhinal cortex during a location memory task. We find that grid firing can either be anchored to the task environment, or can encode distance travelled independently of the task reference frame. Anchoring varied between and within sessions, while spatial firing of non-grid cells was either coherent with the grid population, or was stably anchored to the task environment. We took advantage of the variability in task-anchoring to evaluate whether and when encoding of location by grid cells might contribute to behaviour. We find that when reward location is indicated by a visual cue, performance is similar regardless of whether grid cells are task-anchored or not, arguing against a role for grid representations when location cues are available. By contrast, in the absence of the visual cue, performance was enhanced when grid cells were anchored to the task environment. Our results suggest that anchoring of grid cells to task reference frames selectively enhances performance when path integration is required.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Local prediction-learning in high-dimensional spaces enables neural networks to plan

Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.

A perspective on neuroethology: what the past teaches us about the future of neuroethology

For 100 years, the Journal of Comparative Physiology-A has significantly supported research in the field of neuroethology. The celebration of the journal's centennial is a great time point to appreciate the recent progress in neuroethology and to discuss possible avenues of the field. Animal behavior is the main source of inspiration for neuroethologists. This is illustrated by the huge diversity of investigated behaviors and species. To explain behavior at a mechanistic level, neuroethologists combine neuroscientific approaches with sophisticated behavioral analysis. The rapid technological progress in neuroscience makes neuroethology a highly dynamic and exciting field of research. To summarize the recent scientific progress in neuroethology, I went through all abstracts of the last six International Congresses for Neuroethology (ICNs 2010-2022) and categorized them based on the sensory modalities, experimental model species, and research topics. This highlights the diversity of neuroethology and gives us a perspective on the field's scientific future. At the end, I highlight three research topics that may, among others, influence the future of neuroethology. I hope that sharing my roots may inspire other scientists to follow neuroethological approaches.

Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward

During goal-directed navigation, 'what' information, describing the experiences occurring in periods surrounding a reward, can be combined with spatial 'where' information to guide behavior and form episodic memories. This integrative process likely occurs in the hippocampus, which receives spatial information from the medial entorhinal cortex; however, the source of the 'what' information is largely unknown. Here, we show that mouse lateral entorhinal cortex (LEC) represents key experiential epochs during reward-based navigation tasks. We discover separate populations of neurons that signal goal approach and goal departure and a third population signaling reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning the new reward location. Therefore, the LEC contains a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the medial entorhinal cortex.

Lost in space(s): Multimodal neuroimaging of disorientation along the Alzheimer's disease continuum

Orientation is a fundamental cognitive faculty and the bedrock of the neurologic examination. Orientation is defined as the alignment between an individual's internal representation and the external world in the spatial, temporal, and social domains. While spatial disorientation is a recognized hallmark of Alzheimer's disease (AD), little is known about disorientation beyond space in AD. This study aimed to explore disorientation in spatial, temporal, and social domains along the AD continuum. Fifty-one participants along the AD continuum performed an ecological orientation task in the spatial, temporal, and social domains while undergoing functional MRI. Disorientation in AD followed a three-way association between orientation domain, brain region, and disease stage. Specifically, patients with early amnestic mild cognitive impairment exhibited spatio-temporal disorientation and reduced brain activity in temporoparietal regions, while patients with AD dementia showed additional social disorientation and reduced brain activity in frontoparietal regions. Furthermore, patterns of hypoactivation overlapped different subnetworks of the default mode network, patterns of fluorodeoxyglucose hypometabolism, and cortical atrophy characteristic of AD. Our results suggest that AD may encompass a disorder of orientation, characterized by a biphasic process manifesting as early spatio-temporal and late social disorientation. As such, disorientation may offer a unique window into the clinicopathological progression of AD. SIGNIFICANCE STATEMENT: Despite extensive research into Alzheimer's disease (AD), its core cognitive deficit remains a matter of debate. In this study, we investigated whether orientation, defined as the ability to align internal representations with the external world in spatial, temporal, and social domains, constitutes a core cognitive deficit in AD. To do so, we used PET-fMRI imaging to collect behavioral, functional, and metabolic data from 51 participants along the AD continuum. Our findings suggest that AD may constitute a disorder of orientation, characterized by an early spatio-temporal disorientation and followed by late social disorientation, manifesting in task-evoked and neurodegenerative changes. We propose that a profile of disorientation across multiple domains offers a unique window into the progression of AD and as such could greatly benefit disease diagnosis, monitoring, and evaluation of treatment response.

Evidence for shallow cognitive maps in schizophrenia

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

Grid codes underlie multiple cognitive maps in the human brain

Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes-empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.

Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure

To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task's complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called "cognitive map". However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent "splitters") while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.

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

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

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization 1 . However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior 2,3 . Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

Sharpening the blades of the dentate gyrus: how adult-born neurons differentially modulate diverse aspects of hippocampal learning and memory

For decades, the mammalian hippocampus has been the focus of cellular, anatomical, behavioral, and computational studies aimed at understanding the fundamental mechanisms underlying cognition. Long recognized as the brain's seat for learning and memory, a wealth of knowledge has been accumulated on how the hippocampus processes sensory input, builds complex associations between objects, events, and space, and stores this information in the form of memories to be retrieved later in life. However, despite major efforts, our understanding of hippocampal cognitive function remains fragmentary, and models trying to explain it are continually revisited. Here, we review the literature across all above-mentioned domains and offer a new perspective by bringing attention to the most distinctive, and generally neglected, feature of the mammalian hippocampal formation, namely, the structural separability of the two blades of the dentate gyrus into "supra-pyramidal" and "infra-pyramidal". Next, we discuss recent reports supporting differential effects of adult neurogenesis in the regulation of mature granule cell activity in these two blades. We propose a model for how differences in connectivity and adult neurogenesis in the two blades can potentially provide a substrate for subtly different cognitive functions.

Simple and complex cells revisited: toward a selectivity-invariance model of object recognition

This paper presents a theoretical perspective on modeling ventral stream processing by revisiting the computational abstraction of simple and complex cells. In parallel to David Marr's vision theory, we organize the new perspective into three levels. At the computational level, we abstract simple and complex cells into space partitioning and composition in a topological space based on the redundancy exploitation hypothesis of Horace Barlow. At the algorithmic level, we present a hierarchical extension of sparse coding by exploiting the manifold constraint in high-dimensional space (i.e., the blessing of dimensionality). The resulting over-parameterized models for object recognition differ from existing hierarchical models by disentangling the objectives of selectivity and invariance computation. It is possible to interpret our hierarchical construction as a computational implementation of cortically local subspace untangling for object recognition and face representation, which are closely related to exemplar-based and axis-based coding in the medial temporal lobe. At the implementation level, we briefly discuss two possible implementations based on asymmetric sparse autoencoders and divergent spiking neural networks.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

The orbitofrontal cortex: A goal-directed cognitive map framework for social and non-social behaviors

The orbitofrontal cortex (OFC) is regarded as one of the core brain areas in a variety of value-based behaviors. Over the past two decades, tremendous knowledge about the OFC function was gained from studying the behaviors of single subjects. As a result, our previous understanding of the OFC's function of encoding decision variables, such as the value and identity of choices, has evolved to the idea that the OFC encodes a more complex representation of the task space as a cognitive map. Accumulating evidence also indicates that the OFC importantly contributes to behaviors in social contexts, especially those involved in cooperative interactions. However, it remains elusive how exactly OFC neurons contribute to social functions and how non-social and social behaviors are related to one another in the computations performed by OFC neurons. In this review, we aim to provide an integrated view of the OFC function across both social and non-social behavioral contexts. We propose that seemingly complex functions of the OFC may be explained by its role in providing a goal-directed cognitive map to guide a wide array of adaptive reward-based behaviors in both social and non-social domains.

Testing cognitive theories with multivariate pattern analysis of neuroimaging data

Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the 'how' of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.

The thalamic reuniens is associated with consolidation of non-spatial memory too

The nucleus reuniens (RE) is situated in the midline thalamus and provides a key link between the hippocampus and prefrontal cortex. This anatomical relationship positions the Re as an ideal candidate to facilitate memory consolidation. However, there is no evidence that this role extends beyond spatial memory and contextual fear memory, which are both strongly associated with hippocampal function. We, therefore, trained intact male Long-Evans rats on an odor-trace-object paired-associate task where the explicit 10-s delay between paired items renders the task sensitive to hippocampal function. Neurons in the RE showed significantly increased activation of the immediate early gene (Zif268) when rats were re-tested for previous non-spatial memory 25 days after acquisition training, compared to a group tested at 5-days post-acquisition, as well as a control group tested 25 days after acquisition but with a new pair of non-spatial stimuli, and home cage controls. The remote recall group also showed relatively augmented IEG expression in the superficial layers of the medial PFC (anterior cingulate cortex and prelimbic cortex). These findings support the conclusion that the RE is preferentially engaged during remote recall in this non-spatial task and thus has a role beyond spatial memory and contextual fear memory.

The effect of memory load on object reconstruction: Insights from an online mouse-tracking task

Why can't we remember everything that we experience? Previous work in the domain of object memory has suggested that our ability to resolve interference between relevant and irrelevant object features may limit how much we can remember at any given moment. Here, we developed an online mouse-tracking task to study how memory load influences object reconstruction, testing participants synchronously over virtual conference calls. We first tested up to 18 participants concurrently, replicating memory findings from a condition where participants were tested individually. Next, we examined how memory load influenced mouse trajectories as participants reconstructed target objects. We found interference between the contents of working memory and what was perceived during object reconstruction, an effect that interacted with visual similarity and memory load. Furthermore, we found interference from previously studied but currently irrelevant objects, providing evidence of object-to-location binding errors. At the greatest memory load, participants were nearly three times more likely to move their mouse cursor over previously studied nontarget objects, an effect observed primarily during object reconstruction rather than in the period before the final response. As evidence of the dynamic interplay between working memory and perception, these results show that object reconstruction behavior may be altered by (i) interference between what is represented in mind and what is currently being viewed, and (ii) interference from previously studied but currently irrelevant information. Finally, we discuss how mouse tracking can provide a rich characterization of participant behavior at millisecond temporal resolution, enormously increasing power in cognitive psychology experiments.

Neural representation dynamics reveal computational principles of cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass task representation learning. The formation of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.

Computational cross-species views of the hippocampal formation

The discovery of place cells and head direction cells in the hippocampal formation of freely foraging rodents has led to an emphasis of its role in encoding allocentric spatial relationships. In contrast, studies in head-fixed primates have additionally found representations of spatial views. We review recent experiments in freely moving monkeys that expand upon these findings and show that postural variables such as eye/head movements strongly influence neural activity in the hippocampal formation, suggesting that the function of the hippocampus depends on where the animal looks. We interpret these results in the light of recent studies in humans performing challenging navigation tasks which suggest that depending on the context, eye/head movements serve one of two roles-gathering information about the structure of the environment (active sensing) or externalizing the contents of internal beliefs/deliberation (embodied cognition). These findings prompt future experimental investigations into the information carried by signals flowing between the hippocampal formation and the brain regions controlling postural variables, and constitute a basis for updating computational theories of the hippocampal system to accommodate the influence of eye/head movements.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 sharp-wave ripples (SWRs) during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and SWRs differ from those of dorsal CA1. We identified three clusters of visual cortical excitatory neurons that are excited together with either dorsal or intermediate CA1 SWRs, or suppressed before both SWRs. Neurons in each cluster were distributed across primary and higher visual cortices and co-active even in the absence of SWRs. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence: (i) suppression of SWR-suppressed cortical neurons, (ii) thalamic silence, and (iii) activation of the cortical ensemble preceding and predicting intermediate CA1 SWRs. We propose that the coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

Entropy and Cross-Level Orderliness in Light of the Interconnection between the Neural System and Consciousness

Despite recent advances, the origin and utility of consciousness remains under debate. Using an evolutionary perspective on the origin of consciousness, this review elaborates on the promising theoretical background suggested in the temporospatial theory of consciousness, which outlines world-brain alignment as a critical predisposition for controlling behavior and adaptation. Such a system can be evolutionarily effective only if it can provide instant cohesion between the subsystems, which is possible only if it performs an intrinsic activity modified in light of the incoming stimulation. One can assume that the world-brain interaction results in a particular interference pattern predetermined by connectome complexity. This is what organisms experience as their exclusive subjective state, allowing the anticipation of regularities in the environment. Thus, an anticipative system can emerge only in a regular environment, which guides natural selection by reinforcing corresponding reactions and decreasing the system entropy. Subsequent evolution requires complicated, layered structures and can be traced from simple organisms to human consciousness and society. This allows us to consider the mode of entropy as a subject of natural evolution rather than an individual entity.

Imagination: The dawn of consciousness: Fighting some misconceptions in the discussion about consciousness

Several theories of consciousness (ToC) have been proposed, but it is hard to integrate them into a consensus theory. Each theory has its merits, in dealing with some aspects of the question, but the terminology is inconsistent, each ToC aims at answering a different question, and there is not even a reasonable agreement about what 'consciousness' is in the first place. Some common implicit assumptions, and the way some critical words - such as 'sensation', 'perception', 'neural correlate of consciousness' (NCC) - are thought to relate to consciousness, have introduced a series of misconceptions that make it difficult to pinpoint what consciousness consists in and how it arises in the brain. The purpose of this contribution is twofold: firstly, to discern the various steps that lead from the detection of a stimulus to a conscious experience, by redefining terms such as sensation and perception with an adequate operative meaning; secondly, to emphasize the inevitable contribution of emotions and the active role of imagination in this process. The diffuse view, for the layperson but among scientists as well, is that the brain produces an internal 'representation' of the external reality and of oneself. This tends to consign one to a Cartesian perspective, i.e., the idea that some entity must be there to witness and interpret such representation. This approach splits the conscious experience into brain activity, which generates a (possible) content of consciousness (still unconscious), and a vaguely defined entity or process that 'generates' consciousness and injects (or sheds the light of) consciousness onto the content of brain activity. This way, however, we learn nothing about how such consciousness would arise. We propose here that consciousness is the function that generates a subjectively relevant and emotionally coloured internal image of the experience one is living. In this process, endogenous, spontaneous activity (imaginative activity, consisting in recalling and reviving memories, prefiguring consequences, analysing conjectures) produces many vague and ambiguous hints, rich of symbolic links, which compete in giving rise to an implicit, emotionally characterized, and semantically pleiotropic, internal experience. Cognitive elaboration may extract from this a defined and univocal, complete and consistent, explicit experience, that can be verbally reported ('what it is like to...').

Lost in space(s): multimodal neuroimaging of disorientation along the Alzheimer's disease continuum

Orientation is a fundamental cognitive faculty, allowing the behaving self to link his/her current state to their internal representations of the external world. Once exclusively linked to knowledge of the current place and present time, in recent years, the concept of orientation has evolved to include processing of social, temporal, and abstract relations. Concordantly with the growing focus on orientation, spatial disorientation has been increasingly recognized as a hallmark symptom of Alzheimer's disease (AD). However, few studies have sought to explore disorientation along the AD continuum beyond the spatial domain. 51 participants along the AD continuum performed an orientation task in the spatial, temporal and social domains. Under functional magnetic resonance imaging (fMRI), participants determined which of two familiar places/events/people is geographically/ chronologically/ socially closer to them, respectively. A series of analyses revealed disorientation along the AD- continuum to follow a three-way association between (1) orientation domain, (2) brain region, and (3) disease stage. Specifically, participants with MCI exhibited impaired spatio-temporal orientation and reduced task-evoked activity in temporoparietal regions, while participants with AD dementia exhibited impaired social orientation and reduced task-evoked activity in frontoparietal regions. Furthermore, these patterns of hypoactivation coincided with Default Mode Network (DMN) sub-networks, with spatio-temporal orientation activation overlapping DMN-C and social orientation with DMN-A. Finally, these patterns of disorientation- associated hypoactivations coincided with patterns of fluorodeoxyglucose (FDG) hypometabolism and cortical atrophy characteristic to AD-dementia. Taken together, our results suggest that AD may constitute a disorder of orientation, characterized by a biphasic process as (1) early spatio-temporal and (2) late social disorientation, concurrently manifesting in task-evoked and neurodegenerative changes in temporoparietal and parieto-frontal brain networks, respectively. We propose that a profile of disorientation across multiple domains offers a unique window into the progression of AD.

Hippocampal representations of foraging trajectories depend upon spatial context

Animals learn trajectories to rewards in both spatial, navigational contexts and relational, non-navigational contexts. Synchronous reactivation of hippocampal activity is thought to be critical for recall and evaluation of trajectories for learning. Do hippocampal representations differentially contribute to experience-dependent learning of trajectories across spatial and relational contexts? In this study, we trained mice to navigate to a hidden target in a physical arena or manipulate a joystick to a virtual target to collect delayed rewards. In a navigational context, calcium imaging in freely moving mice revealed that synchronous CA1 reactivation was retrospective and important for evaluation of prior navigational trajectories. In a non-navigational context, reactivation was prospective and important for initiation of joystick trajectories, even in the same animals trained in both contexts. Adaptation of trajectories to a new target was well-explained by a common learning algorithm in which hippocampal activity makes dissociable contributions to reinforcement learning computations depending upon spatial context.