Place cells on a maze encode routes rather than destinations

Abstract representations emerge in human hippocampal neurons during inference

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 generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. 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 performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. 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 behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.

The homogenous hippocampus: How hippocampal cells process available and potential goals

We present here a view of the firing patterns of hippocampal cells that is contrary, both functionally and anatomically, to conventional wisdom. We argue that the hippocampus responds to efference copies of goals encoded elsewhere; and that it uses these to detect and resolve conflict or interference between goals in general. While goals can involve space, hippocampal cells do not encode spatial (or other special types of) memory, as such. We also argue that the transverse circuits of the hippocampus operate in an essentially homogeneous way along its length. The apparently different functions of different parts (e.g. memory retrieval versus anxiety) result from the different (situational/motivational) inputs on which those parts perform the same fundamental computational operations. On this view, the key role of the hippocampus is the iterative adjustment, via Papez-like circuits, of synaptic weights in cell assemblies elsewhere.

Activity-driven synaptic translocation of LGI1 controls excitatory neurotransmission

The fine control of synaptic function requires robust trans-synaptic molecular interactions. However, it remains poorly understood how trans-synaptic bridges change to reflect the functional states of the synapse. Here, we develop optical tools to visualize in firing synapses the molecular behavior of two trans-synaptic proteins, LGI1 and ADAM23, and find that neuronal activity acutely rearranges their abundance at the synaptic cleft. Surprisingly, synaptic LGI1 is primarily not secreted, as described elsewhere, but exo- and endocytosed through its interaction with ADAM23. Activity-driven translocation of LGI1 facilitates the formation of trans-synaptic connections proportionally to the history of activity of the synapse, adjusting excitatory transmission to synaptic firing rates. Accordingly, we find that patient-derived autoantibodies against LGI1 reduce its surface fraction and cause increased glutamate release. Our findings suggest that LGI1 abundance at the synaptic cleft can be acutely remodeled and serves as a critical control point for synaptic function.

Rodent maze studies: from following simple rules to complex map learning

More than 100 years since the first maze designed for rodent research, researchers now have the choice of a variety of mazes that come in many different shapes and sizes. Still old designs get modified and new designs are introduced to fit new research questions. Yet, which maze is the most optimal to use or which training paradigm should be applied, remains up for debate. In this review, we not only provide a historical overview of maze designs and usages in rodent learning and memory research, but also discuss the possible navigational strategies the animals can use to solve each maze. Furthermore, we summarize the different phases of learning that take place when a maze is used as the experimental task. At last, we delve into how training and maze design can affect what the rodents are actually learning in a spatial task.

Conjunctive encoding of exploratory intentions and spatial information in the hippocampus

The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal's exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals' investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.

Navigation and the efficiency of spatial coding: insights from closed-loop simulations

Spatial learning is critical for survival and its underlying neuronal mechanisms have been studied extensively. These studies have revealed a wealth of information about the neural representations of space, such as place cells and boundary cells. While many studies have focused on how these representations emerge in the brain, their functional role in driving spatial learning and navigation has received much less attention. We extended an existing computational modeling tool-chain to study the functional role of spatial representations using closed-loop simulations of spatial learning. At the heart of the model agent was a spiking neural network that formed a ring attractor. This network received inputs from place and boundary cells and the location of the activity bump in this network was the output. This output determined the movement directions of the agent. We found that the navigation performance depended on the parameters of the place cell input, such as their number, the place field sizes, and peak firing rate, as well as, unsurprisingly, the size of the goal zone. The dependence on the place cell parameters could be accounted for by just a single variable, the overlap index, but this dependence was nonmonotonic. By contrast, performance scaled monotonically with the Fisher information of the place cell population. Our results therefore demonstrate that efficiently encoding spatial information is critical for navigation performance.

Hippocampal sequences span experience relative to rewards

Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.

A goal pointer for a cognitive map in the orbitofrontal cortex

Knowing where you are and where you go is a prerequisite for planning a goal-directed journey. The discovery of spatially tuned neurons in the hippocampus and parahippocampal cortices provides a mechanism by which the brain pinpoints an animal’s own position in an environment. By contrast, how the brain encodes a remote navigational goal remained largely obscure until recently. In this review, we discuss algorithmic challenges and requirements for the brain to form a representation of a remote navigational goal at which an animal is not present. We then highlight a line of evidence that neurons in the orbitofrontal cortex (OFC) represent a goal location persistently while an animal navigates to this destination. Finally, we propose a new perspective of navigation research opened by this recently reported brain’s goal map.

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.

A map of spatial navigation for neuroscience

Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.

Anatomical organization of temporally correlated neural calcium activity in the hippocampal CA1 region

Hippocampal CA1 neuronal ensembles generate sequential patterns of firing activity that contribute to episodic memory formation and spatial cognition. Here we used in vivo calcium imaging to record neural ensemble activities in mouse hippocampal CA1 and identified CA1 excitatory neuron sub-populations whose members are active across the same second-long period of time. We identified groups of hippocampal neurons sharing temporally correlated neural calcium activity during behavioral exploration and found that they also organized as clusters in anatomical space. Such clusters vary in membership and activity dynamics with respect to movement in different environments, but also appear during immobility in the dark suggesting an internal dynamic. The strong covariance between dynamics and anatomical location within the CA1 sub-region reveals a previously unrecognized form of topographic representation in hippocampus that may guide generation of hippocampal sequences across time and therefore organize the content of episodic memory.

Learning to predict future locations with internally generated theta sequences

Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, we reported recently that the position represented by CA1 place cells at a given theta phase corresponds to the location where animals were or will be located at a fixed time interval into the past or future assuming the animal ran at its typical, not the current, speed through that part of the environment. This coding scheme leads to longer theta trajectories, larger place fields and shallower phase precession in areas where animals typically run faster. Here we present a mechanistic computational model that accounts for these experimental observations. The model consists of a continuous attractor network with short-term synaptic facilitation and depression that internally generates theta sequences that advance at a fixed pace. Spatial locations are then mapped onto the active units via modified Hebbian plasticity. As a result, neighboring units become associated with spatial locations further apart where animals run faster, reproducing our earlier experimental results. The model also accounts for the higher density of place fields generally observed where animals slow down, such as around rewards. Furthermore, our modeling results reveal that an artifact of the decoding analysis might be partly responsible for the observation that theta trajectories start behind the animal's current position. Overall, our results shed light on how the hippocampal code might arise from the interplay between behavior, sensory input and predefined network dynamics.

Temporal context and latent state inference in the hippocampal splitter signal

The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central goal is to understand what the core computations supporting these functions are, and how these computations are realized in the collective action of single neurons. A potential access point into this issue is provided by 'splitter cells', hippocampal neurons that fire differentially on the overlapping segment of trajectories that differ in their past and/or future. However, the literature on splitter cells has been fragmented and confusing, owing to differences in terminology, behavioral tasks, and analysis methods across studies. In this review, we synthesize consistent findings from this literature, establish a common set of terms, and translate between single-cell and ensemble perspectives. Most importantly, we examine the combined findings through the lens of two major theoretical ideas about hippocampal function: representation of temporal context and latent state inference. We find that unique signature properties of each of these models are necessary to account for the data, but neither theory, by itself, explains all of its features. Specifically, the temporal gradedness of the splitter signal is strong support for temporal context, but is hard to explain using state models, while its flexibility and task-dependence is naturally accounted for using state inference, but poses a challenge otherwise. These theories suggest a number of avenues for future work, and we believe their application to splitter cells is a timely and informative domain for testing and refining theoretical ideas about hippocampal function.

Experience-dependent changes in hippocampal spatial activity and hippocampal circuit function are disrupted in a rat model of Fragile X Syndrome

BACKGROUND

Fragile X syndrome (FXS) is a common single gene cause of intellectual disability and autism spectrum disorder. Cognitive inflexibility is one of the hallmarks of FXS with affected individuals showing extreme difficulty adapting to novel or complex situations. To explore the neural correlates of this cognitive inflexibility, we used a rat model of FXS (Fmr1^-/y).

METHODS

We recorded from the CA1 in Fmr1^-/y and WT littermates over six 10-min exploration sessions in a novel environment-three sessions per day (ITI 10 min). Our recordings yielded 288 and 246 putative pyramidal cells from 7 WT and 7 Fmr1^-/y rats, respectively.

RESULTS

On the first day of exploration of a novel environment, the firing rate and spatial tuning of CA1 pyramidal neurons was similar between wild-type (WT) and Fmr1^-/y rats. However, while CA1 pyramidal neurons from WT rats showed experience-dependent changes in firing and spatial tuning between the first and second day of exposure to the environment, these changes were decreased or absent in CA1 neurons of Fmr1^-/y rats. These findings were consistent with increased excitability of Fmr1^-/y CA1 neurons in ex vivo hippocampal slices, which correlated with reduced synaptic inputs from the medial entorhinal cortex. Lastly, activity patterns of CA1 pyramidal neurons were dis-coordinated with respect to hippocampal oscillatory activity in Fmr1^-/y rats.

LIMITATIONS

It is still unclear how the observed circuit function abnormalities give rise to behavioural deficits in Fmr1^-/y rats. Future experiments will focus on this connection as well as the contribution of other neuronal cell types in the hippocampal circuit pathophysiology associated with the loss of FMRP. It would also be interesting to see if hippocampal circuit deficits converge with those seen in other rodent models of intellectual disability.

CONCLUSIONS

In conclusion, we found that hippocampal place cells from Fmr1^-/y rats show similar spatial firing properties as those from WT rats but do not show the same experience-dependent increase in spatial specificity or the experience-dependent changes in network coordination. Our findings offer support to a network-level origin of cognitive deficits in FXS.

Neural modulations in the auditory cortex during internal and external attention tasks: A single-patient intracranial recording study

Brain sensory processing is not passive, but is rather modulated by our internal state. Different research methods such as non-invasive imaging methods and intracranial recording of the local field potential (LFP) have been used to study to what extent sensory processing and the auditory cortex in particular are modulated by selective attention. However, at the level of the single- or multi-units the selective attention in humans has not been tested. In addition, most previous research on selective attention has explored externally-oriented attention, but attention can be also directed inward (i.e., internal attention), like spontaneous self-generated thoughts and mind-wandering. In the present study we had a rare opportunity to record multi-unit activity (MUA) in the auditory cortex of a patient. To complement, we also analyzed the LFP signal of the macro-contact in the auditory cortex. Our experiment consisted of two conditions with periodic beeping sounds. The participants were asked either to count the beeps (i.e., an "external attention" condition) or to recall the events of the previous day (i.e., an "internal attention" condition). We found that the four out of seven recorded units in the auditory cortex showed increased firing rates in "external attention" compared to "internal attention" condition. The beginning of this attentional modulation varied across multi-units between 30-50 msec and 130-150 msec from stimulus onset, a result that is compatible with an early selection view. The LFP evoked potential and induced high gamma activity both showed attentional modulation starting at about 70-80 msec. As the control, for the same experiment we recorded MUA activity in the amygdala and hippocampus of two additional patients. No major attentional modulation was found in the control regions. Overall, we believe that our results provide new empirical information and support for existing theoretical views on selective attention and spontaneous self-generated cognition.

Neuronal signature of spatial decision-making during navigation by freely moving rats by using calcium imaging

A challenge in spatial memory is understanding how place cell firing contributes to decision-making in navigation. A spatial recency task was created in which freely moving rats first became familiar with a spatial context over several days and thereafter were required to encode and then selectively recall one of three specific locations within it that was chosen to be rewarded that day. Calcium imaging was used to record from more than 1,000 cells in area CA1 of the hippocampus of five rats during the exploration, sample, and choice phases of the daily task. The key finding was that neural activity in the startbox rose steadily in the short period prior to entry to the arena and that this selective population cell firing was predictive of the daily changing goal on correct trials but not on trials in which the animals made errors. Single-cell and population activity measures converged on the idea that prospective coding of neural activity can be involved in navigational decision-making.

How to build a cognitive map

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal-cortical interactions and beyond.

Adaptive cognitive maps for curved surfaces in the 3D world

Terrains in a 3D world can be undulating. Yet, most prior research has exclusively investigated spatial representations on a flat surface, leaving a 2D cognitive map as the dominant model in the field. Here, we investigated whether humans represent a curved surface by building a dimension-reduced flattened 2D map or a full 3D map. Participants learned the location of objects positioned on a flat and curved surface in a virtual environment by driving on the concave side of the surface (Experiment 1), driving and looking vertically (Experiment 2), or flying (Experiment 3). Subsequently, they were asked to retrieve either the path distance or the 3D Euclidean distance between the objects. Path distance estimation was good overall, but we found a significant underestimation bias for the path distance on the curve, suggesting an influence of potential 3D shortcuts, even though participants were only driving on the surface. Euclidean distance estimation was better when participants were exposed more to the global 3D structure of the environment by looking and flying. These results suggest that the representation of the 2D manifold, embedded in a 3D world, is neither purely 2D nor 3D. Rather, it is flexible and dependent on the behavioral experience and demand.

Spatial goal coding in the hippocampal formation

The mammalian hippocampal formation contains several distinct populations of neurons involved in representing self-position and orientation. These neurons, which include place, grid, head direction, and boundary-vector cells, are thought to collectively instantiate cognitive maps supporting flexible navigation. However, to flexibly navigate, it is necessary to also maintain internal representations of goal locations, such that goal-directed routes can be planned and executed. Although it has remained unclear how the mammalian brain represents goal locations, multiple neural candidates have recently been uncovered during different phases of navigation. For example, during planning, sequential activation of spatial cells may enable simulation of future routes toward the goal. During travel, modulation of spatial cells by the prospective route, or by distance and direction to the goal, may allow maintenance of route and goal-location information, supporting navigation on an ongoing basis. As the goal is approached, an increased activation of spatial cells may enable the goal location to become distinctly represented within cognitive maps, aiding goal localization. Lastly, after arrival at the goal, sequential activation of spatial cells may represent the just-taken route, enabling route learning and evaluation. Here, we review and synthesize these and other evidence for goal coding in mammalian brains, relate the experimental findings to predictions from computational models, and discuss outstanding questions and future challenges.

Higher-Order Conditioning in the Spatial Domain

Spatial learning and memory, the processes through which a wide range of living organisms encode, compute, and retrieve information from their environment to perform goal-directed navigation, has been systematically investigated since the early twentieth century to unravel behavioral and neural mechanisms of learning and memory. Early theories about learning to navigate space considered that animals learn through trial and error and develop responses to stimuli that guide them to a goal place. According to a trial-and error learning view, organisms can learn a sequence of motor actions that lead to a goal place, a strategy referred to as response learning, which contrasts with place learning where animals learn locations with respect to an allocentric framework. Place learning has been proposed to produce a mental representation of the environment and the cartesian relations between stimuli within it-which Tolman coined the cognitive map. We propose to revisit some of the best empirical evidence of spatial inference in animals, and then discuss recent attempts to account for spatial inferences within an associative framework as opposed to the traditional cognitive map framework. We will first show how higher-order conditioning can successfully account for inferential goal-directed navigation in a variety of situations and then how vectors derived from path integration can be integrated via higher-order conditioning, resulting in the generation of higher-order vectors that explain novel route taking. Finally, implications to cognitive map theories will be discussed.

Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.

The grid code for ordered experience

Entorhinal cortical grid cells fire in a periodic pattern that tiles space, which is suggestive of a spatial coordinate system. However, irregularities in the grid pattern as well as responses of grid cells in contexts other than spatial navigation have presented a challenge to existing models of entorhinal function. In this Perspective, we propose that hippocampal input provides a key informative drive to the grid network in both spatial and non-spatial circumstances, particularly around salient events. We build on previous models in which neural activity propagates through the entorhinal-hippocampal network in time. This temporal contiguity in network activity points to temporal order as a necessary characteristic of representations generated by the hippocampal formation. We advocate that interactions in the entorhinal-hippocampal loop build a topological representation that is rooted in the temporal order of experience. In this way, the structure of grid cell firing supports a learned topology rather than a rigid coordinate frame that is bound to measurements of the physical world.

The Prefrontal-Hippocampal Comparator: Volition and Episodic Memory

This review describes recent research that is relevant to the prefrontal-hippocampal comparator model with the following conclusions: 1. Hippocampal area CA1 serves, at least in part, as an associative match-mismatch comparator. 2. Voluntary movement strengthens episodic memories for goal-directed behavior. 3. Hippocampal theta power serves as a prediction error signal during hippocampal dependent tasks. 4. The self-referential component of episodic memory in humans is mediated by the corollary discharge (the efference copy of the action plan developed by prefrontal cortex and transmitted to hippocampus where it is stored as a working memory; CA1 uses this efference copy to compare the expected consequences of action to the actual consequences of action). 5. Impairments in the production or transmission of this corollary discharge may contribute to some of the symptoms of schizophrenia. Unresolved issues and suggestions for future research are discussed.

Navigating for reward

An organism's survival can depend on its ability to recall and navigate to spatial locations associated with rewards, such as food or a home. Accumulating research has revealed that computations of reward and its prediction occur on multiple levels across a complex set of interacting brain regions, including those that support memory and navigation. However, how the brain coordinates the encoding, recall and use of reward information to guide navigation remains incompletely understood. In this Review, we propose that the brain's classical navigation centres - the hippocampus and the entorhinal cortex - are ideally suited to coordinate this larger network by representing both physical and mental space as a series of states. These states may be linked to reward via neuromodulatory inputs to the hippocampus-entorhinal cortex system. Hippocampal outputs can then broadcast sequences of states to the rest of the brain to store reward associations or to facilitate decision-making, potentially engaging additional value signals downstream. This proposal is supported by recent advances in both experimental and theoretical neuroscience. By discussing the neural systems traditionally tied to navigation and reward at their intersection, we aim to offer an integrated framework for understanding navigation to reward as a fundamental feature of many cognitive processes.

Do hippocampal pyramidal cells respond to nonspatial stimuli?

There are currently a number of theories of rodent hippocampal function. They fall into two major groups that differ in the role they impute to space in hippocampal information processing. On one hand, the cognitive map theory sees space as crucial and central, with other types of nonspatial information embedded in a primary spatial framework. On the other hand, most other theories see the function of the hippocampal formation as broader, treating all types of information as equivalent and concentrating on the processes carried out irrespective of the specific material being represented, stored, and manipulated. One crucial difference, therefore, is the extent to which theories see hippocampal pyramidal cells as representing nonspatial information independently of a spatial framework. Studies have reported the existence of single hippocampal unit responses to nonspatial stimuli, both to simple sensory inputs as well as to more complex stimuli such as objects, conspecifics, rewards, and time, and these findings been interpreted as evidence in favor of a broader hippocampal function. Alternatively, these nonspatial responses might actually be feature-in-place signals where the spatial nature of the response has been masked by the fact that the objects or features were only presented in one location or one spatial context. In this article, we argue that when tested in multiple locations, the hippocampal response to nonspatial stimuli is almost invariably dependent on the animal's location. Looked at collectively, the data provide strong support for the cognitive map theory.

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps

Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

Decoding route selection of pigeon during goal-directed behavior: A joint spike-LFP study

How to reach the goal is one of the core problems that animals must solve to complete goal-directed behavior. Studies have proved the important role of hippocampus (Hp) in spatial navigation and shown that hippocampal neural activities can represent the current location and goal location. However, for the different routes linking these two locations, the neural representation mechanism of the route selection in Hp is not clear. Here, we addressed this question using neural recordings of Hp ensembles and decoding analyses in pigeons performing a goal-directed route selection task known to require Hp participation. The hippocampal spike trains and local field potentials (LFPs) of five pigeons performing the task were acquired and analyzed. We found that the neuron firing rates and power spectrum characteristics in Hp could encode the animal's route selection during goal-directed behavior, suggesting that the representation of route selection was coherent for hippocampal spike and LFP signals. Decoding results further indicated that joint spike-LFP features resulted in a significant improvement in the representation accuracy of the route selection. These findings of this study will help to understand the encoding mechanism of route selection in goal-directed behavior.

Structuring Knowledge with Cognitive Maps and Cognitive Graphs

Humans and animals use mental representations of the spatial structure of the world to navigate. The classical view is that these representations take the form of Euclidean cognitive maps, but alternative theories suggest that they are cognitive graphs consisting of locations connected by paths. We review evidence suggesting that both map-like and graph-like representations exist in the mind/brain that rely on partially overlapping neural systems. Maps and graphs can operate simultaneously or separately, and they may be applied to both spatial and nonspatial knowledge. By providing structural frameworks for complex information, cognitive maps and cognitive graphs may provide fundamental organizing schemata that allow us to navigate in physical, social, and conceptual spaces.

Hippocampal spatial memory representations in mice are heterogeneously stable

The population of hippocampal neurons actively coding space continually changes across days as mice repeatedly perform tasks. Many hippocampal place cells become inactive while other previously silent neurons become active, challenging the idea that stable behaviors and memory representations are supported by stable patterns of neural activity. Active cell replacement may disambiguate unique episodes that contain overlapping memory cues, and could contribute to reorganization of memory representations. How active cell replacement affects the evolution of representations of different behaviors within a single task is unknown. We trained mice to perform a delayed nonmatching to place task over multiple weeks, and performed calcium imaging in area CA1 of the dorsal hippocampus using head-mounted miniature microscopes. Cells active on the central stem of the maze "split" their calcium activity according to the animal's upcoming turn direction (left or right), the current task phase (study or test), or both task dimensions, even while spatial cues remained unchanged. We found that, among reliably active cells, different splitter neuron populations were replaced at unequal rates, resulting in an increasing number of cells modulated by turn direction and a decreasing number of cells with combined modulation by both turn direction and task phase. Despite continual reorganization, the ensemble code stably segregated these task dimensions. These results show that hippocampal memories can heterogeneously reorganize even while behavior is unchanging.

Spatial inference without a cognitive map: the role of higher-order path integration

The cognitive map has been taken as the standard model for how agents infer the most efficient route to a goal location. Alternatively, path integration - maintaining a homing vector during navigation - constitutes a primitive and presumably less-flexible strategy than cognitive mapping because path integration relies primarily on vestibular stimuli and pace counting. The historical debate as to whether complex spatial navigation is ruled by associative learning or cognitive map mechanisms has been challenged by experimental difficulties in successfully neutralizing path integration. To our knowledge, there are only three studies that have succeeded in resolving this issue, all showing clear evidence of novel route taking, a behaviour outside the scope of traditional associative learning accounts. Nevertheless, there is no mechanistic explanation as to how animals perform novel route taking. We propose here a new model of spatial learning that combines path integration with higher-order associative learning, and demonstrate how it can account for novel route taking without a cognitive map, thus resolving this long-standing debate. We show how our higher-order path integration (HOPI) model can explain spatial inferences, such as novel detours and shortcuts. Our analysis suggests that a phylogenetically ancient, vector-based navigational strategy utilizing associative processes is powerful enough to support complex spatial inferences.

Local Design Principles at Hippocampal Synapses Revealed by an Energy-Information Trade-Off

Synapses across different brain regions display distinct structure-function relationships. We investigated the interplay of fundamental design constraints that shape the transmission properties of the excitatory CA3-CA1 pyramidal cell connection, a prototypic synapse for studying the mechanisms of learning in the mammalian hippocampus. This small synapse is characterized by probabilistic release of transmitter, which is markedly facilitated in response to naturally occurring trains of action potentials. Based on a physiologically motivated computational model of the rat CA3 presynaptic terminal, we show how unreliability and short-term dynamics of vesicular release work together to regulate the trade-off of information transfer versus energy use. We propose that individual CA3-CA1 synapses are designed to operate near the maximum possible capacity of information transmission in an efficient manner. Experimental measurements reveal a wide range of vesicular release probabilities at hippocampal synapses, which may be a necessary consequence of long-term plasticity and homeostatic mechanisms that manifest as presynaptic modifications of the release probability. We show that the timescales and magnitude of short-term plasticity (STP) render synaptic information transfer nearly independent of differences in release probability. Thus, individual synapses transmit optimally while maintaining a heterogeneous distribution of presynaptic strengths indicative of synaptically-encoded memory representations. Our results support the view that organizing principles that are evident on higher scales of neural organization percolate down to the design of an individual synapse.

Enhancement of Hippocampal Spatial Decoding Using a Dynamic Q-Learning Method With a Relative Reward Using Theta Phase Precession

Hippocampal place cells and interneurons in mammals have stable place fields and theta phase precession profiles that encode spatial environmental information. Hippocampal CA1 neurons can represent the animal's location and prospective information about the goal location. Reinforcement learning (RL) algorithms such as Q-learning have been used to build the navigation models. However, the traditional Q-learning ([Formula: see text]Q-learning) limits the reward function once the animals arrive at the goal location, leading to unsatisfactory location accuracy and convergence rates. Therefore, we proposed a revised version of the Q-learning algorithm, dynamical Q-learning ([Formula: see text]Q-learning), which assigns the reward function adaptively to improve the decoding performance. Firing rate was the input of the neural network of [Formula: see text]Q-learning and was used to predict the movement direction. On the other hand, phase precession was the input of the reward function to update the weights of [Formula: see text]Q-learning. Trajectory predictions using [Formula: see text]Q- and [Formula: see text]Q-learning were compared by the root mean squared error (RMSE) between the actual and predicted rat trajectories. Using [Formula: see text]Q-learning, significantly higher prediction accuracy and faster convergence rate were obtained compared with [Formula: see text]Q-learning in all cell types. Moreover, combining place cells and interneurons with theta phase precession improved the convergence rate and prediction accuracy. The proposed [Formula: see text]Q-learning algorithm is a quick and more accurate method to perform trajectory reconstruction and prediction.

Hippocampal state transitions at the boundaries between trial epochs

The hippocampus encodes distinct contexts with unique patterns of activity. Representational shifts with changes in context, referred to as remapping, have been extensively studied. However, less is known about transitions between representations. In this study, we leverage a large dataset of neuronal recordings taken while rats performed an olfactory memory task with a predictable temporal structure involving trials and intertrial intervals (ITIs), separated by salient boundaries at the trial start and trial end. We found that trial epochs were associated with stable hippocampal representations despite moment-to-moment variability in stimuli and behavior. Representations of trial and ITI epochs were far more distinct than spatial factors would predict and the transitions between the two were abrupt. The boundary was associated with a large spike in multiunit activity, with many individual cells specifically active at the start or end of each trial. Both epochs and boundaries were encoded by hippocampal populations, and these representations carried information on orthogonal axes readily identified using principal component analysis. We suggest that the hippocampus orthogonalizes representations of the trial and ITI epochs and the activity spike at trial boundaries might serve to drive hippocampal activity from one stable state to the other.

Three brain states in the hippocampus and cortex

Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet, despite this critique, the stimulus-driven paradigm still dominates-possibly because a convincing alternative has not been clear. Here, we review a series of findings suggesting such an alternative. These findings indicate that neural activity in the hippocampus occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different functional roles in cognition. This three-state framework also indicates that neural representations in the hippocampus follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in the cortex, suggesting shared principles and brain-wide organization of intrinsic neural activity.

A stable home-base promotes allocentric memory representations of episodic-like everyday spatial memory

A key issue in neurobiological studies of episodic-like memory is the geometric frame of reference in which memory traces of experience are stored. Assumptions are sometimes made that specific protocols favour either allocentric (map-like) or egocentric (body-centred) representations. There are, however, grounds for suspecting substantial ambiguity about coding strategy, including the necessity to use both frames of reference occasionally, but tests of memory representation are not routinely conducted. Using rats trained to find and dig up food in sandwells at a particular place in an event arena (episodic-like 'action-where' encoding), we show that a protocol previously thought to foster allocentric encoding is ambiguous but more predisposed towards egocentric encoding. Two changes in training protocol were examined with a view to promoting preferential allocentric encoding-one in which multiple start locations were used within a session as well as between sessions; and another that deployed a stable home-base to which the animals had to carry food reward. Only the stable home-base protocol led to excellent choice performance which rigorous analyses revealed to be blocked by occluding extra-arena cues when this was done after encoding but before recall. The implications of these findings for studies of episodic-like memory are that the representational framework of memory at the start of a recall trial will likely include a path direction in the egocentric case but path destination in the allocentric protocol. This difference should be observable in single-unit recording or calcium-imaging studies of spatially-tuned cells.

Hippocampal Place Cells Encode Local Surface-Texture Boundaries

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

Anticipatory feelings: Neural correlates and linguistic markers

This review introduces anticipatory feelings (AF) as a new construct related to the process of anticipation and prediction of future events. AF, defined as the state of awareness of physiological and neurocognitive changes that occur within an oganism in the form of a process of adapting to future events, are an important component of anticipation and expectancy. They encompass bodily-related interoceptive and affective components and are influenced by intrapersonal and dispositional factors, such as optimism, hope, pessimism, or worry. In the present review, we consider evidence from animal and human research, including neuroimaging studies, to characterize the brain structures and brain networks involved in AF. The majority of studies reviewed revealed three brain regions involved in future oriented feelings: 1) the insula; 2) the ventromedial prefrontal cortex (vmPFC); and 3) the amygdala. Moreover, these brain regions were confirmed by a meta-analysis, using a platform for large-scale, automated synthesis of fMRI data. Finally, by adopting a neurolinguistic and a big data approach, we illustrate how AF are expressed in language.

Single-Neuron Representations of Spatial Targets in Humans

The hippocampus and surrounding medial-temporal-lobe (MTL) structures are critical for both memory and spatial navigation, but we do not fully understand the neuronal representations used to support these behaviors. Much research has examined how the MTL neurally represents spatial information, such as with "place cells" that represent an animal's current location or "head-direction cells" that code for an animal's current heading. In addition to behaviors that require an animal to attend to the current spatial location, navigating to remote destinations is a common part of daily life. To examine the neural basis of these behaviors, we recorded single-neuron activity from neurosurgical patients playing Treasure Hunt, a virtual-reality spatial-memory task. By analyzing how the activity of these neurons related to behavior in Treasure Hunt, we found that the firing rates of many MTL neurons during navigation significantly changed depending on the position of the current spatial target. In addition, we observed neurons whose firing rates during navigation were tuned to specific heading directions in the environment, and others whose activity changed depending on the timing within the trial. By showing that neurons in our task represent remote locations rather than the subject's own position, our results suggest that the human MTL can represent remote spatial information according to task demands.

Distinct effects of reward and navigation history on hippocampal forward and reverse replays

To better understand the functional roles of hippocampal forward and reverse replays, we trained rats in a spatial sequence memory task and examined how these replays are modulated by reward and navigation history. We found that reward enhances both forward and reverse replays during the awake state, but in different ways. Reward enhances the rate of reverse replays, but it increases the fidelity of forward replays for recently traveled as well as other alternative trajectories heading toward a rewarding location. This suggests roles for forward and reverse replays in reinforcing representations for all potential rewarding trajectories. We also found more faithful reactivation of upcoming than already rewarded trajectories in forward replays. This suggests a role for forward replays in preferentially reinforcing representations for high-value trajectories. We propose that hippocampal forward and reverse replays might contribute to constructing a map of potential navigation trajectories and their associated values (a "value map") via distinct mechanisms.

A non-spatial account of place and grid cells based on clustering models of concept learning

One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leading to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.

Neural Representation of Overlapping Path Segments and Reward Acquisitions in the Monkey Hippocampus

Disambiguation of overlapping events is thought to be the hallmark of episodic memory. Recent rodent studies have reported that when navigating overlapping path segments in the different routes place cell activity in the same overlapping path segments were remapped according to different goal locations in different routes. However, it is unknown how hippocampal neurons disambiguate reward delivery in overlapping path segments in different routes. In the present study, we recorded monkey hippocampal neurons during performance of three virtual navigation (VN) tasks in which a monkey alternately navigated two different routes that included overlapping path segments (common central hallway) and acquired rewards in the same locations in overlapping path segments by manipulating a joystick. The results indicated that out of 106 hippocampal neurons, 57 displayed place-related activity (place-related neurons), and 18 neurons showed route-dependent activity in the overlapping path segments, consistent with a hippocampal role in the disambiguation of overlapping path segments. Moreover, 75 neurons showed neural correlates to reward delivery (reward-related neurons), whereas 56 of these 75 reward-related neurons showed route-dependent reward-related activity in the overlapping path segments. The ensemble activity of reward-related neurons represented reward delivery, locations, and routes in the overlapping path segments. In addition, ensemble activity patterns of hippocampal neurons more distinctly represented overlapping path segments than non-overlapping path segments. The present results provide neurophysiological evidence of disambiguation in the monkey hippocampus, consistent with a hippocampal role in episodic memory, and support a recent computational model of "neural differentiation," in which overlapping items are better represented by repeated retrieval with competitive learning.

In a Temporally Segmented Experience Hippocampal Neurons Represent Temporally Drifting Context But Not Discrete Segments

There is widespread agreement that episodic memory is organized into a timeline of past experiences. Recent work suggests that the hippocampus may parse the flow of experience into discrete episodes separated by event boundaries. A complementary body of work suggests that context changes gradually as experience unfolds. We recorded from hippocampal neurons as male Long-Evans rats performed 6 blocks of an object discrimination task in sets of 15 trials. Each block was separated by removal from the testing chamber for a delay to enable segmentation. The reward contingency reversed from one block to the next to incentivize segmentation. We expected animals to hold two distinct, recurring representations of context to match the two distinct rule contingencies. Instead, we found that overtrained rats began each block neither above nor below chance but by guessing randomly. While many units had clear firing fields selective to the conjunction of objects in places, a significant population also reflected a continuously drifting code both within block and across blocks. Despite clear boundaries between blocks, we saw no neural evidence for event segmentation in this experiment. Rather, the hippocampal ensemble drifted continuously across time. This continuous drift in the neural representation was consistent with the lack of segmentation observed in behavior.SIGNIFICANCE STATEMENT The neuroscience literature yet to reach consensus on how the hippocampus supports the organization of events across time in episodic memory. Initial studies reported stable hippocampal maps segmented by remapping events. However, it remains unclear whether segmentation is an artifact of cue responsivity. Recently, research has shown that the hippocampal code exhibits continuous drift. Drift may represent a continually evolving context; however, it is unclear whether this is an artifact of changing experiences. We recorded dCA1 in rats performing an object discrimination task designed to segment time. Overtrained rats could not anticipate upcoming context switches but used context boundaries to their advantage. Hippocampal ensembles showed neither evidence of alternating between stable contexts nor sensitivity to boundaries, but showed robust temporal drift.

A novel low-noise movement tracking system with real-time analog output for closed-loop experiments

BACKGROUND

Modern electrophysiological experiments are moving towards closing the loop, where the extrinsic (behavioral) and intrinsic (neuronal) variables automatically affect stimulation parameters. Rodent experiments targeting spatial behavior require animal 2D kinematics to be continuously monitored in a reliable and accurate manner. Cameras provide a robust, flexible, and simple way to track kinematics on the fly. Indeed, several available camera-based systems yield high spatiotemporal resolution. However, the acquired kinematic data cannot be accessed with sufficient temporal resolution for precise real-time feedback.

NEW METHOD

Here, we describe a novel software and hardware system for movement tracking based on color-markers with real-time low-noise output that works in both light and dark conditions. The analog outputs precisely represent 2D movement features including position, orientation, and their temporal derivatives, velocity and angular velocity.

RESULTS

Using adaptive windowing, contour extraction, and rigid-body Kalman filtering, a 640-by-360 pixel frame is processed in 28 ms with less than 4 ms jitter, for 100 frames per second. The system is robust to outliers, has low noise, and maintains a smooth, accurate output even when one or more markers are temporarily missing. Using freely-moving mice, we demonstrate novel applications such as replacing conventional sensors in a behavioral arena and inducing novel place fields via closed-loop optogenetic stimulation.

COMPARISON WITH EXISTING METHOD(S)

To the best of our knowledge, this is the first tracking system that yields analog output in real-time.

CONCLUSIONS

This modular system for closed-loop experiment tracking can be implemented by downloading an open-source software and assembling low-cost hardware circuity.

Insensitivity of Place Cells to the Value of Spatial Goals in a Two-Choice Flexible Navigation Task

Hippocampal place cells show position-specific activity thought to reflect a self-localization signal. Several reports also point to some form of goal encoding by place cells. We investigated this by asking whether they also encode the value of spatial goals, which is crucial information for optimizing goal-directed navigation. We used a continuous place navigation task in which male rats navigate to one of two (freely chosen) unmarked locations and wait, triggering the release of reward, which is then located and consumed elsewhere. This allows sampling of place fields and dissociates spatial goal from reward consumption. The two goals varied in the amount of reward provided, allowing assessment of whether the rats factored goal value into their navigational choice and of possible neural correlates of this value. Rats successfully learned the task, indicating goal localization, and they preferred higher-value goals, indicating processing of goal value. Replicating previous findings, there was goal-related activity in the out-of-field firing of CA1 place cells, with a ramping-up of firing rate during the waiting period, but no general overrepresentation of goals by place fields, an observation that we extended to CA3 place cells. Importantly, place cells were not modulated by goal value. This suggests that dorsal hippocampal place cells encode space independently of its associated value despite the effect of that value on spatial behavior. Our findings are consistent with a model of place cells in which they provide a spontaneously constructed value-free spatial representation rather than encoding other navigationally relevant but nonspatial information.SIGNIFICANCE STATEMENT We investigated whether hippocampal place cells, which compute a self-localization signal, also encode the relative value of places, which is essential information for optimal navigation. When choosing between two spatial goals of different value, rats preferred the higher-value goal. We saw out-of-field goal firing in place cells, replicating previous observations that the cells are influenced by the goal, but their activity was not modulated by the value of these goals. Our results suggest that place cells do not encode all of the navigationally relevant aspects of a place, but instead form a value-free "map" that links to such aspects in other parts of the brain.